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Recommender System for Crops & Varieties Selection
A Farmer Centered Approach
DISSERTATION SUBMITTED TO THE GLOBAL OPEN UNIVERSITY
OF NAGLAND
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE AWARD OF THE DEGREE OF
MASTER OF PHILOSOPHY
BY
GAURAV KUMAR AGARWAL
DEPARTMENT OF COMPUTER SCIENCE
GLOBAL OPEN UNIVERSITY
NAGALAND
INDIA
2009
CERTIFICATE
This is to certify that the work embodied in this dissertation entitled RECOMMENDER
SYSTEM FOR CROPS & VARIETIES SELECTION – A FARMER CENTERED
APPROACH has been submitted to Department of Computer Science, Global Open
University, Nagaland. The work presented in this dissertation has not been submitted in
parts or full to any other university for any diploma or degree.
Gaurav Kumar Agarwal
(Candidate)
Dr. Sudeep Marwaha
(Supervisor and Scientist (SS)
Division of ComputerApplications IASRI,
Pusa, Delhi)
ii
Abstract
As information mounts it leads to the problem of how to access, navigate through,
and select available options. One possible solution to the problem is based on recommender
systems or the concept of automatic recommendation generation. A recommender system
recommends items to users by predicting items relevant to the user, based on various kinds
of information including items, user information and interactions between users and items.
Recommender systems form a specific type of information filtering technique that attempt
to present information items such as movies, music, books, news, images, web pages, that
are likely to be of interest to the user. Typically, a recommender system compares the
user's profile to some reference characteristics. These characteristics may be from the
information item (the content-based approach) or the user's social environment (the
collaborative filtering approach) or combination of both (Hybrid-filtering approach). These
systems are very powerful cognitive decision – makers in the context of distributed online
information processing in real – time networked scenarios.
Recommender Systems have now evolved to trust – based recommender systems where the
trust component has been modeled using soft computing techniques. Thus the
recommender systems have graduated from being intelligent decision – support systems to
human – level AI systems. Trust plays an important role in the decisions related to sources
that are used by humans to take recommendations. We do not go out on the street and just
start taking recommendations from anyone on the street, but opt for our trustworthy
acquaintances to suggest us products. In real life, we use the data provided by others, but
process it according to our own reasoning power, into information that we use for decision
making. If recommender systems also use advice seeking and decision making process
similar to real life, then it will be easier for the user to trust the recommendations of the
system. Hence, by adding a trust component to a decentralized environment the problem of
lack of trust on the recommenders is alleviated.
There are number of domain experts (agriculture scientists) each having knowledge of their
domain according to their own perspective. Now, if the farmer wants information about the
iii
varieties of a crop that can be grown in a specific zone such as Uttar Pradesh, he may
contact the domain experts who provide their knowledge of the specific problem area such
as pathology (Disease related problems) or entomology (insect/pest problems). Thereafter
accordingly the farmer processes the information gathered according to his specification.
As the experts may be geographically distributed, this knowledge engineering problem is a
distributed computing problem.
Research literature on intelligent agent system architectures has established that problems
that are inherently distributed can be efficiently implemented as a multi agent system. So
we propose a prototype recommender system for agriculture that helps the farmers to find
the best possible crops / set of varieties which can be grown on the farmer’s field. This
prototype recommender system is based on a multi-agent system approach, where every
user in the prototype is represented by an agent. The agents communicate with others to
generate the recommendation using various techniques like Content Based, Collaborative
and Hybrid approach.
.
iv
Acknowledgements
It gives me immense pleasure to extend my thanks to those who
provided inestimable support for the completion of my work.
In the first place I would like to record my gratitude to Dr. Sudeep
Marwaha for his supervision, advice, guidance from the very early stage of
this research and for patiently guiding me through the dissertation process. I
gratefully acknowledge him for his crucial contributions, which made him a
backbone of this dissertation.
I would like to thank my brother Mr. Sumit Agarwal for the valuable
suggestions he had provided during this work.
Further, I would like to thank Mrs. Jotsna, staff of Global Open
University Nagaland, for her prompt help whenever I approached her.
I thank my parents, and in-laws, for their faith in me and allowing me to
be as ambitious as I wanted.
I owe my loving thanks to my wife Mrs. Madhulika Agarwal, for all the
support and encouragement she had provided in spite of her hectic schedule. I
would also thank my little son Aryan Agarwal for letting me work on this
study by being patient and understanding.
I would like to thank all the people who have directly or indirectly
helped me in completing this dissertation.
GAURAV KUMAR AGARWAL
v
Table of Contents
Abstract ....................................................................................................................... i
Acknowledgement ................................................................................................ iii
List of Figures ......................................................................................................... vi
List of Tables .......................................................................................................... vii Chapter 1 Introduction ....................................................................................... 1 1.1 Problem Definition ................................................................................. 2 1.2 Overview of Dissertation ........................................................................ 2 Chapter 2 Review of Literature .................................................................... 4 2.1 Introduction ............................................................................................ 4 2.2 Working of Recommender System ......................................................... 5 2.3 Classification of the existing Recommender Systems ............................ 6 2.4 Techniques used by different types of Recommender Systems .............. 6 2.5 Evaluation of Recommender Systems ...................................................10 2.6 Technologies Used .................................................................................12 2.6.1 Multi Agent System ...................................................................12 2.6.2 JADE (Java Agent Development Environment) ........................13 2.6.3 APACHE TOMCAT (Web Server) ...........................................13 2.6.4 JAVA .........................................................................................13 2.6.5 JSP (Java Server Pages) .............................................................14 2.6.6 ORACLE 9i ............................................................................................ 15 Chapter 3 System Design ................................................................................ 17 3.1 Introduction ............................................................................................17 3.2 Basics of Intuitionistic Fuzzy Sets .........................................................17 3.3 Improving Recommender Systems with Trust .......................................19 3.4 Analysis .................................................................................................22 3.5 Methodology ..........................................................................................22 3.6 Algorithm ...............................................................................................27
vi
Chapter 4 System Architecture of Recommender system .......... 32 4.1 Introduction ............................................................................................32 4.2 System Architecture ...............................................................................33 4.3 Database Design ....................................................................................36 4.4 Implementation ......................................................................................44 Chapter 5 Conclusion ......................................................................................... 47 Annexure I ................................................................................................................. 49
Database Table Description .............................................................49 Annexure II .............................................................................................77 Abbreviations .....................................................................................77 References .................................................................................................................. 85
vii
LIST OF FIGURES Figure No
Description
Page No
1 Working of Recommender System 5
2 Web of Trust 20
3 Functional diagram of A RECOMMENDER SYSTEM-AFCA 23
4 Block Diagram of AFCA 33
5 Data Flow Diagram 34
6 Entity - Relationship Diagram 35
7 Login window of AFCA 41
8 Agent Environment 42
9 Information exchange among the agents during unintentional encounters
42
10 Selection of user’s known variety list 43
11 User’s known variety details 43
12 Selection of experienced varieties 44
13 Ranking of varieties according to the variety attributes 44
14 Updation of trust of the user over recommenders 45
15 Grade the degree of significance 45
16 Selection of zone for generating the recommendation 46
17 Recommendation Report 46
viii
LIST OF TABLES Table No
Description
Page No
1 Degree of significance of attributes of varieties
25
2 Degree of Trust of user on the recommenders
25
3 Preference List (all the sublists) about taste of user maintained by recommenders
26
4 Uncertain List (UA) about taste of user maintained by recommenders
26
ix
Chapter 1
Introduction The availability of sea of alternatives in terms of products, movies, books, holiday
destinations, etc. have resulted in predicament for today’s man. It is possible to fish out
the best option from this sea, only if we are prepared to devote enough time and/or
money to find it. We prefer a shortcut by taking support from society to select a good
alternative. The support from the society is in the form of recommendations from our
acquaintances, reviews in the newspapers, magazines, and general surveys, etc. to help us
find interesting products or services. As information becomes abundant and multi-
dimensional, humans are confronted with more difficult decisions about how to access,
navigate through, and select available options. The sheer number of alternatives often
makes a wise choice impossible without some intelligent computational assistance. The
need for such assistance led to the development of the Recommender Systems.
Recommender Systems attempt to reduce information overload and retain customers by
selecting a subset of items from a universal set based on user preferences. While research
in recommender systems grew out of information retrieval and filtering, the topic has
steadily advanced into a legitimate and challenging research area of its own. The
Recommender Systems are a technological proxy for the support provided by the friends,
reviews in the magazines, newspapers, etc. to filter the set of possible options to a more
manageable subset and act like electronic assistants to sort out relevant information for
the user. These are a class of applications that use opinions of members of a community,
to help individuals in that community, identify information or products most likely to be
interesting to them or relevant to their needs. Typically, recommender system analyses
data about items’ or about interactions between users and items to find the associations
amongst them. The results obtained are used to predict relevant and interesting items for
the user.
10
The research on recommender systems uses the results of a wide variety of fields like
Information Retrieval, Artificial Intelligence, Cognitive Science, Approximation Theory,
Forecasting Theories, Statistics, etc.
1.1 Problem Definition Recommender Systems have come up in the market to ensure that the right information is
delivered to the right person at the right time. They use opinions of members of a
community to help individuals in that community identify information most likely to be
interesting to them or relevant to their needs. India being an agriculture base country with
over 70% people gets their livelihood from agriculture and allied industry. With an ever
increasing demand for food and government’s pledge for food security thrusts researchers
to innovate support tools for agriculture community. In this direction, a prototype
recommender system for agriculture is proposed that helps the farmers to find the best
possible crops / set of varieties which can be grown on the farmer’s field. This system
produces the results based on the knowledge of several domain experts, which
incorporates into the system. The system also provides reports like how many times a
user chooses a given recommendation, what the quality of rating is given by an expert,
etc. Finally system helps in the process of selection of crops and varieties most suited for
a given situation.
1.2 Overview of the Dissertation Chapter 1 of this research work gives an introduction to what is a Recommender
system. It also defines the ‘problem’.
Chapter 2 of this research work reviews the history and recent developments in
the field of Recommender System, classification and working of the existing
Recommender Systems, various techniques used by the Recommender Systems and
evaluation metrics of the Recommender System. It also explains briefly the technologies
used in developing a system for this research work. Some of the technologies are JSP,
JADE, ORACLE 9i, JAVA SCRIPT, APACHE TOMCAT etc.
11
Chapter 3 explains basic concepts of Intuitionistic Fuzzy Sets, Trust and
Recommender System based on Trust. It also explains Analysis, Methodology,
Algorithms used in the proposed recommender system.
Chapter 4 gives the architecture of the system, the database design of the system.
It also shows the implementation details of the proposed recommender system for a given
zone (state) along with the results obtained.
Chapter 5 gives the conclusion and the probable future enhancement for the
system developed.
12
Chapter 2
Review of Literature 2.1 Introduction
The availability of sea of alternatives in terms of products, movies, books, holiday
destinations, etc. have resulted in predicament for today’s man. It is possible to fish out
the best option from this sea, only if we are prepared to devote enough time and/or
money to find it. We prefer a shortcut by taking support from society to select a good
alternative. The support from the society is in the form of recommendations from our
acquaintances, reviews in the newspapers, magazines, and general surveys, etc. to help us
find interesting products or services. As information becomes abundant and multi-
dimensional, humans are confronted with more difficult decisions about how to access,
navigate through, and select available options. The sheer number of alternatives often
makes a wise choice impossible without some intelligent computational assistance. The
need for such assistance led to the development of the Recommender Systems [Resnick
and Varian, 1997].
Recommender Systems attempt to reduce information overload and retain customers by
selecting a subset of items from a universal set based on user preferences. While research
in recommender systems grew out of information retrieval and filtering, the topic has
steadily advanced into a legitimate and challenging research area of its own.
The Recommender Systems are a technological proxy for the support provided by the
friends, reviews in the magazines, newspapers, etc. to filter the set of possible options to
a more manageable subset and act like electronic assistants to sort out relevant
information for the user [Schafer et. al., 1999]. These are a class of applications that use
opinions of members of a community, to help individuals in that community, identify
information or products most likely to be interesting to them or relevant to their needs.
Typically, recommender system analyses data about items’ or about interactions between
13
users and items to find the associations amongst them. The results obtained are used to
predict relevant and interesting items for the user.
Recommender Systems have come up in the market to ensure that the right
information is delivered to the right person at the right time. They use opinions of
members of a community to help individuals in that community identify information
most likely to be interesting to them or relevant to their needs.
2.2 Working of Recommender System
According to [Burke, 2002; Schafer et. al., 2002], the principal mode of operation of the
recommender systems can be broken down into three major steps as:
1. Acquisition of the background information which is in the form of the explicit ratings
given by the users to the products or preferences of the users learnt by the system
through the history of purchase transactions, usage records, etc.
2. Determination of the pattern of associations among the users or items, from the
background information.
3. Generation of the predicted ratings or the recommendations for the target user from
the patterns found in the background information.
Figure 1: Working of Recommender Systems
Background Information (Product Ratings, User Profiles, etc.)
Patterns of association among users or items
Recommendations for the target user
14
Generating recommendations basically is a problem of estimating ratings for the items that
the target user has not seen. Intuitively, this estimation is based on the ratings given by the
target user to the other items; or on the ratings given by the other users who are similar to
the target user in terms of preferences.
2.3 Classification of the existing Recommender Systems
The recommender systems can be classified mainly into three categories: content based,
collaborative filtering based and hybrid recommender system.
1. Content based recommender systems
These recommender systems use the ratings of the items the target user liked in
the past to predict items the target user would also like.
2. Collaborative filtering based recommender systems
These recommender systems use the ratings of the users with tastes “similar” to
the target user and predict items for the target user.
3. Hybrid recommender systems
Hybrid recommender systems use a combination of the above two approaches.
2.4 Techniques used by different types of Recommender Systems
The different types of recommender systems require data to be analyzed to find the
patterns of associations among the users or the items. The following analysis techniques
are used to find the associations:
• Neighborhood formation
• Association Rule Mining
• Machine Learning
15
2.4.1 Neighborhood Formation
When systems look for similar users or items purchased together, they form a
neighborhood of the users most similar to the target user or of items that are generally
purchased together [Sarwar et. al., 2001; Semeraro et. al., 2005]. The neighbors of the
target user or item are determined using different methods such as correlation coefficient;
similarity functions, clustering techniques, etc.
Once the value of similarity is available through correlation coefficient or similarity
functions, etc., the neighbors of the target user or the item are selected using any of the
following method:
• Compare the values obtained against a threshold and those that exceed the threshold
form a part of the neighborhood.
• Select fixed number of best neighbors of the target. The best neighbors are the ones
that are most similar to the target user or item [Baldi et. al., 2003].
Once the neighborhood has been selected, the ratings from the neighborhood are
combined to predict ratings for the target user of the products that he/she is not familiar
with.
Value of similarity can be calculated by the following methods:
2.4.1.1 Correlation Coefficient
The Pearson’s correlation coefficient r(A, B), between two users A and B is given by
( ) ( )( )( )( ) ( )( )∑ ∑∑ ∑
∑∑∑=2
B2B
2A
2A
BABA
R - Rn R - Rn
RR - RRn B) r(A,
Where,
RA and RB are the rating vectors of A and B, respectively of size n.
16
2.4.1.2 Similarity Functions
The cosine similarity function is the most common similarity function used to determine
the similarity between two entities. The ratings/ attributes of the entities are considered as
vectors and the cosine formula calculates the cosine of the angle between the two vectors.
As cosine approaches “1”, the two vectors become coincident. If two vectors are totally
unrelated, they will be orthogonal and the cosine will be “0”. The similarity between the
two entities →
u and →
v is defined as follows:
22
v u
v u v ,ucos )v ,u sim(→→
→→→→→→ •
=⎟⎠⎞
⎜⎝⎛=
Where i
→→
• v u is the dot product of vectors →→
v and u .
2.4.1.3 Clustering
Clustering techniques work by identifying groups of users who appear to have similar
preferences. The clusters are usually created using distance metrics. Once the clusters are
created, the estimated rating for the target user can be computed as the rating of the
centroid user by averaging the ratings of the others users in that cluster [Ungar and
Foster, 1998; Li and Kim, 2003; Semeraro et. al., 2005].
Two broad approaches used in literature for clustering are as follows:
• Partitional methods
• Hierarchical methods
2.4.2 Association Rule Mining
The association rule mining method is used to discover elements that co-occur frequently
within the dataset, e.g., purchasing transactions and to discover rules, such as implication
or correlation, which relate co-occurring. Questions like “If a customer purchases product
17
A, how likely is he/she to purchase product B?” and “What products are likely to be
purchased by a customer if he/she has purchased items C and D?” are answered by
association finding algorithms. Association rules are not restricted to dependency
analysis in the context of retail applications, but are also successfully applied to a wide
variety of business problems.
An association rule is an expression X ⇒ Y, where X and Y are set of items. Given
database D of transactions, where each transaction DT ∈ is a set containing items X, Y.
X ⇒ Y expresses that whenever a transaction T contains X then T probably contains Y
also. The probability or rule confidence is defined as the percentage of transactions
containing Y in addition to X with regard to the overall number of transactions containing
X. That is, the rule confidence can be understood as the conditional
probability ( )TTp X | Y ⊆⊆ .
2.4.3 Machine Learning
Machine learning deals with the development of algorithms and techniques that allow
computers to “learn”. There are two types of learning: inductive and deductive. Inductive
machine learning methods create computer programs by extracting rules and patterns out
of massive data sets. The algorithms used include Bayesian networks, neural networks,
etc.
In content based recommender systems, the patterns of association are computed for
the dataset related to the target user and in collaborative filtering recommender systems,
the patterns of association are computed for the dataset consisting of transactions related
to all the users of the system. Using the learnt patterns, products are recommended to the
target user.
2.5 Improving Recommender Systems with Trust
Research in recommender systems to date has predominately focused on designing
algorithms for more effective and efficient computation of recommendations [Breese et.
al., 1998; Sarwar et. al., 2000b]. Even though the recommender systems may be
providing accurate recommendations, studies by Sinha and Swearingen [2001] have
18
found that users prefer the recommendations of friends over those of recommender
systems. This is because most of the existing recommender systems do not take into
account the social elements of advice seeking and decision-making. Hence the system
model does not match the mental model of the user and it is difficult for the user to trust
the system recommendations. [Bedi P. and Kaur H, 2006 ] [Bedi P., Kaur H and Marwaha
S., 2007 ] [Bonhard, 2004][Kaur H, 2007, Phd Thesis ]
Augmenting recommender systems with trust dimension in a decentralized
environment solves the problem of lack of trust on the recommender system. Trust plays
an important role in the decisions related to sources that are used by humans to take
recommendations. We do not go out on the street and just start taking recommendations
from anyone on the street, but opt for our trustworthy acquaintances to suggest us
products. In real life, we use the data provided by others, but process it according to our
own reasoning power, into information that we use for decision making. If recommender
systems also use advice seeking and decision making process similar to real life, then it
will be easier for the user to trust recommendations of the system. Hence, by adding trust
to decentralized environment alleviate the problem of lack of trust on the recommender
systems.
2.6 Evaluation of Recommender Systems
A wide variety of algorithms exist for generating recommendations and their
evaluation Metrics are required to empirically measure how a recommender system
predicted ranking of items to the user’s true preferences. They may also measure how
well a system can predict an exact rating for a specific product. In literature, majority of
the evaluations of the recommender system are the done using accuracy metrics to prove
the utility of the recommender system [Herlocker et. al., 2004].
2.6.1 Predictive Accuracy Metrics
Predictive accuracy metrics measure the closeness of the predicted ratings to the true user
ratings. Most prominent and widely used mean absolute error (MAE) represents an
efficient means to measure the statistical accuracy of the predictions [Shardanand and
Maes, 1995; Breese et. al., 1998]. The accuracy of m predictions is as follows:
19
m
Real - Pred MAE
m
1 iii∑
==
2.6.2 Decision-Support Metrics
The decision support metrics do not consider rating predictions and their deviations from
the actual ratings; rather they judge how relevant the ranked recommendations are for the
user. Precision, recall, fallout and F measure, metrics fall in this category.
Precision
Precision is defined as the fraction of the selected items that are relevant to the user’s
needs. It measures the selection effectiveness of the system and represents the probability
that the item is relevant.
retrievedtionsrecommenda ofnumber Totalretrieved tionsrecommendarelevant ofNumber Precision =
Recall
Recall is defined as the ratio of the relevant items selected to the total number of relevant
items available. Recall represents the probability that a relevant item will be selected.
availabletionsrecommendarelevant ofnumber Totalretrieved tionsrecommendarelevant ofNumber Recall =
Fallout
The fallout is the fraction of the irrelevant items retrieved. Fallout represents the
probability that an irrelevant item is selected.
retrievedtionsrecommenda ofnumber Totalretrieved tionsrecommenda irrelevant ofNumber Fallout =
F-measure
Another metric that is used extensively in information retrieval and recommender
systems research is the F-measure metric [Huang et. al., 2004a]. Many a times, it is
required that a precision and recall be used in conjunction. The F-measure consists of a
20
weighted combination of precision and recall. The general formula for F-measure (for
non-negative real, α) is
( )RecallPrecision
RecallPrecision 1 F 2
2
+×××+
=αα
α
When α = 1, it is known as F1 measure and represents the weighted harmonic mean of
precision and recall giving equal weights to them.
2.7 Technologies Used The system makes use of the following technologies:
Multi-Agent System:
Multi-Agent system focuses on systems in which many intelligent agents interact with
each other. The agents are considered to be autonomous entities, such as software
programs or robots. Their interactions can be either cooperative or selfish. That is, the
agents can share a common goal (e.g. an ant colony), or they can pursue their own
interests (as in the free market economy).
Agents are small software programs that communicate with each other, acting
behaviorally to interact and respond, matching available resources to demand. In a multi-
agent system, each agent communicates with the network of agents, considering options
for matching its capabilities with demand, negotiating on such constraints as quality,
price and time, and then making decisions for committing resources to match demand.
The most important reason to use MAS when designing a system is that some domains
require it. In particular, if there are different people or organizations with different
(possibly conflicting) goals and proprietary information, then a multi agent system is
needed to handle their interactions. Even if each organization wants to model its internal
affairs with a single system, the organizations will not give authority to any single person
to build a system that represents them all: the different organizations will need their own
system that reflects their capabilities and priorities.
21
JADE (Java Agent Development Environment):
JADE (Java Agent Development Environment) is a software framework to develop
agent-based applications in compliance with the FIPA (Foundation for Intelligent
Physical Agents) specifications for interoperable intelligent multi-agent systems. The
goal is to simplify the development while ensuring standard compliance through a
comprehensive set of system services and agents. JADE can then be considered an agent
middle-ware that implements an Agent Platform and a development framework. It deals
with all those aspects that are not peculiar of the agent internals and that are independent
of the applications, such as message transport, encoding and parsing, or agent life-cycle.
The communication architecture offers flexible and efficient messaging, where JADE
creates and manages a queue of incoming ACL messages, private to each agent; agents
can access their queue via a combination of several modes: blocking, polling, timeout and
pattern matching based. The full FIPA communication model has been implemented and
its components have been clearly distinct and fully integrated: interaction protocols,
envelope, ACL, content languages, encoding schemes, ontologies and, finally, transport
protocols. JADE has also been integrated with JESS, a Java shell of CLIPS, in order to
exploit its reasoning capabilities.
Apache Tomcat (Web Server):
Apache Tomcat is a web container or application server developed at the Apache
software foundation (ASF). Tomcat implements the Java Servlets and the Java Server
Pages (JSP) specifications providing an environment for java code to run in cooperation
with a web server.
JAVA:
The implementation of the prototype is carried out in java programming language. Java
can be used to create applications and applets. An application is a program that runs on
your computer, under the operating system of that computer. That is, an application
created by Java is more or less like one created using C or C++. When used to create
applications, Java is not much different from any other computer language. Rather, it is
22
Java’s ability to create applets that makes it important. An applet is an application
designed to be transmitted over the Internet and executed by a Java-compatible Web
browser. An applet is actually a tiny Java program, dynamically downloaded across the
network, just like an image, sound file, or video clip. The important difference is that an
applet is an intelligent program, not just an animation or media file. In other words, an
applet is a program that can react to user input and dynamically change—not just run the
same animation or sound over and over. Java is widely used as its ability to solve the
problems like:
• Security: When you use a Java-compatible Web browser, you can safely
download Java applets without fear of viral infection (viruses, adware, malware,
etc) or malicious intent (hacking passwords / other useful Information). Java
achieves this protection by confining a Java program to the Java execution
environment and not allowing it access to other parts of the computer. The ability
to download applets with confidence that no harm will be done and that no
security will be breached is considered by many to be the single most important
aspect of Java.
• Portability: Java generates the byte code for the application written, once the
byte- code is written it will be easily executed in different platforms with the help
of Java Virtual Machine. Hence, java is considered a portable language.
JSP (Java Server Pages):
Java Server Pages (JSP) technology enables Web developers and designers to rapidly
develop and easily maintain, information-rich, dynamic Web pages that leverage existing
business systems. As part of the Java technology family, JSP technology enables rapid
development of Web-based applications that are platform independent. JSP technology
separates the user interface from content generation, enabling designers to change the
overall page layout without altering the underlying dynamic content.
For a web page developer or designer who is familiar with HTML can:
• Use JSP technology without having to learn the Java language: You can use JSP
technology without learning how to write Java scriplets. Although scriptlets are
23
no longer required to generate dynamic content, they are still supported to provide
backward compatibility.
• Extend the JSP language: Java tag library developers and designers can extend the
JSP language with “simple tag handlers,” which utilize a new, much simpler and
cleaner, tag extension API. This spurs the growing number of pluggable, reusable
tag libraries available, which in turn reduces the amount of code needed to write
powerful Web applications.
• Easily write and maintain pages: The Java Server Pages Standard Tag Library
(JSTL) expression language is now integrated into JSP technology and has been
upgraded to support functions. The expression language can now be used instead
of scriptlet expressions.
JSP technology uses XML-like tags that encapsulate the logic that generates the
content for the page. The application logic can reside in server-based resources (such as
Java Beans component architecture) that the page accesses with these tags. Any and all
formatting (HTML or XML) tags are passed directly back to the response page.
Oracle 9i:
Oracle 9i is a Relational Database Management System used to maintain and store the
knowledge of various domain experts (farmer) in a data format. An Oracle database is a
collection of data treated as a unit. The purpose of a database is to store and retrieve
related information. A database server is the key to solving the problems of information
management. In general, a server reliably manages a large amount of data in a multiuser
environment so that many users can concurrently access the same data. All this is
accomplished while delivering high performance. A database server also prevents
unauthorized access and provides efficient solutions for failure recovery.
Oracle Database is the first database designed for enterprise grid computing, the most
flexible and cost effective way to manage information and applications. Enterprise grid
computing creates large pools of industry-standard, modular storage and servers. With
this architecture, each new system can be rapidly provisioned from the pool of
components. There is no need for peak workloads, because capacity can be easily added
or reallocated from the resource pools as needed.
24
The database has logical structures and physical structures. Because the physical and
logical structures are separate, the physical storage of data can be managed without
affecting the access to logical storage structures.
25
Chapter 3
System Design 3.1 Introduction
When a research is carried out in an organization lot of human effort, time and
money are put into it. After completing the research if the researcher gets to know that
the same research has already been accomplished by another researcher, the investment
made in this research gets wasted. Instead if by some means a researcher can check the
database of accomplished researches, to see all similar researches accomplished earlier,
he will not end up reinventing wheels. This chapter presents the basics of Intuitionistic
fuzzy sets, recommender system based on trust, analysis, methodology and algorithms
used for the research work “A Recommender System for Crops & Varieties Selection-A
farmer Centered Approach”.
3.2 Basics of Intuitionistic Fuzzy Sets
Intuitionistic fuzzy sets (IFS) are one of the interesting and useful generalizations
of fuzzy set theory, introduced by Atanassov (1999) having membership, non-
membership and hesitation part. Fuzzy sets are IFS but the converse is not necessarily
true. IFS theory has been applied in various areas such as logic programming, decision
making problems, medical diagnosis etc.
Here we give some basic definitions, which are used in the paper. Consider a set
E, An intuitionistic fuzzy set (IFS) A in E is defined as an object of the following form
A = {(x, μA(x), νA(x)) | x ∈ E}
where the functions
μA: E → [0,1]
and
νA: E → [0,1]
26
define degree of membership and degree of non-membership of the element x ∈ E,
respectively.
And ∀x ∈ E,
0 ≤ μA + νA ≤ 1
Obviously, each ordinary fuzzy set may be written as
{(x, μA(x), 1– μA(x)) | x ∈ E}
The value of
πA(x) = 1 – μA(x) – νA(x)
is called the degree of non - determinacy (or uncertainty) of the element x ∈ E to the
intuitionistic fuzzy set A. This may cater to either membership value or non-membership
value or both.
3.2.1 Distance between Intuitionistic Fuzzy Sets Two of the most popular distances between the two intuitionistic fuzzy sets A and B in
X = {x1, x2, x3, …, xn} are
• Hamming distance:
lIFS(A,B) = ∑=
n
i 1
( | μA(xi) – μB(xi)| + | νA(xi) – νB(xi)| + | πA(xi) – πB(xi)| )
• Euclidean distance:
eIFS(A,B) = ∑=
n
i 1
( (μA(xi) – μB(xi) )2 + (νA(xi) – νB(xi) )2 + (πA(xi) – πB(xi) )2)1/2
27
3.3 Improving Recommender Systems with Trust
3.3.1 Trust
The presence of trust in human society is unquestionable. All the relations and the
interactions thereof in human society are based on trust between the interacting
individuals. Trust helps an individual to safeguard its interests from harmful individuals
in the society and as agents are designed to behave like humans, trust should play an
important role during the interactions between the agents. The agents also, except for
trivial application domains, require interaction with other agents, i.e., application
domains require multi-agent systems. This interaction is required for sharing files and
services with the other agents in the application domain. Trust increases the robustness of
an agent by making it capable to safeguard its interests from harmful agents in the
application domain as in human beings.
3.3.2 Degree of trust
Trust is not dichotomous; rather it is a range. This means that trust may be graded and the
extent to which one is trustworthy is the degree of trust (DoT) of the entity. Degree of
trust can be defined as a subjective certainty of the pertinent beliefs. We have restricted
the range of degree of trust between 0 and 1.
3.3.3 Recommender System based on Trust
Agents interact with other peer agents in the application domain and exchange
information with each other. An agent forms trustworthy relationships with peers and the
suggestions from such trustworthy acquaintances are used in its decision making process.
These trustworthy relationships form a web of trust. The social network hence formed
helps an agent to get views of even those agents that are not directly known to it through
the agents known to it directly (Fig 2).
28
Fig. 2: Web of Trust
Fig 2 shows such a network of peers represented by the numbered circles, where the
numbers in the circles identify the various peers in the application domain. An edge
represents that trustworthy relationship exists between the connected agents with certain
degree of trust. Every agent maintains a list of peers adjacent to it along with the degrees
of trust on them. It is not necessary that if A trusts B with degree of trust as x then B also
trusts A with degree x.
The peer agents in the community environment exchange recommendations about
the products during their idle time, which we are referring to as the unintentional
encounters. The recommendations are list of products that the recommender has
suggested along with the degree of importance associated with it. An agent may
accumulate the recommendations of one specific agent through more than one
unintentional encounter. But one agent cannot be a part of more than one group at a given
instance. Since the idle agents are free to join the group of agents exchanging
information, such interactions are the unintentional encounters. When an agent wants to
find interesting products for it and explicitly seeks the recommendations from its
trustworthy agents, the interactions are termed as intentional encounters. The
unintentional encounters and their own experience with the products are used by
recommender agents to compute the information for recommendations to be given to the
user agent during the intentional encounters.
It is not feasible for an agent to know every peer in the network to be able to
assign a trust value to them, and then get recommendations from all of them. Rather the
6
98
75
4
3 2
1
29
agent may use the recommendations of those known to it, which may further take the
recommendations from those known to them and so on. This is on the basis of the
assumption that if A trusts B and B trusts C then A also trusts C, even though not
necessarily with the same degree of trust as it has on B. Let the circle with number 1 in
Fig. 2 represents the agent that wants to find interesting products for itself through its
network of acquaintances. All the agents that are directly linked to the user agent
participate in intentional encounters and give their recommendations to the user. Even the
agent that is not directly linked conveys its recommendation list to the user agent through
the intermediate agents.
Research in recommender systems to date has predominately focused on
designing algorithms for more effective and efficient computation of recommendations
[Breese et. al., 1998; Sarwar et. al., 2000b]. Even though the recommender systems may
be providing accurate recommendations, studies by Sinha and Swearingen [2001] have
found that users prefer the recommendations of friends over those of recommender
systems. This is because most of the existing recommender systems do not take into
account the social elements of advice seeking and decision-making, and hence the system
model does not match the mental model of the user [Bonhard, 2004]; and it is difficult
for the user to trust the system recommendations [Bedi P. and Kaur H, 2006 ].
Augmenting recommender systems with trust dimension in a decentralized
environment solves the problem of lack of trust on the recommender system. Trust plays
an important role in the decisions related to sources that are used by humans to take
recommendations. We do not go out on the street and just start taking recommendations
from anyone on the street, but opt for our trustworthy acquaintances to suggest us
products. In real life, we use the data provided by others, but process it according to our
own reasoning power, into information that we use for decision making. If recommender
systems also use advice seeking and decision making process similar to real life, then it
will be easier for the user to trust recommendations of the system. Hence, by adding trust
to decentralized environment alleviate the problem of lack of trust on the recommender
systems.
30
3.4 Analysis of the proposed Recommender System
The existing recommender systems do not generate their recommendations based on the
trust relationships that exist in the society, rather suggest products on the basis of
similarity between the users or the items. They ignore the social elements of decision-
making and advice seeking, and hence the system model does not match the mental
model of the user. The recommender agent does not know about the people whose tastes
are used to suggest products that may be of interest to the user, and this result in lack of
trust on the recommendations received from the system. Given a choice between
recommendations from friends and recommender systems, in terms of quality and
usefulness, friend’s recommendations are preferred even though the recommendations
given by the recommender system have high novelty factor [Sinha et. Al 2000]. Friends
are seen as more qualified to make good and useful recommendations as compared to
recommender systems.
In the presented recommender system, a social structure exists between the agents in the
application domain, which is formed on the basis of trust between them. The agents
recommend varieties to each other using this social structure. This is similar to the mental
model of decision making of a human.
Every agent in the system maintains a degree of trust and information about the tastes of
the agents that are connected to it directly. The recommenders pass only that
recommendation to the user agent that matches its taste leading to personalization of the
recommender system. This reduces the number of recommendations that need to be given
to the user agent by removing the unnecessary recommendations and this further reduces
the number of computations that the user agent has to perform at the aggregation of the
recommendations to find something useful for itself
3.5 Methodology of the proposed Recommender System
The proposed recommender system is a Multi Agent System (MAS) as illustrated in
Fig. 3. Most of the existing recommender systems ignore the social elements of decision-
making and advice seeking, and hence the system model does not match the mental
model of the user. These recommender systems are designed with a central authority
31
controlling the data as well as computational resources to generate recommendations. The
user does not know about the people whose tastes are used to suggest products. This acts
as a hindrance for the user to trust the recommendations of the system.
Fig.3 The Functional diagram of A RECOMMENDER SYSTEM-AFCA
The decentralized and distributed systems are increasingly becoming popular and
the recommender systems can take advantage of the huge amount of information that
they provide. These infrastructures provide the information in human understandable and
machine readable format. Recommender Systems can be designed in such a way that they
use the machine-readable information available with the user or user agent to infer the
user preferences and then generate personalized recommendations for the user.
32
To overcome the limitations of lack of trust and the central control in the existing
recommender systems, a trust based decentralized recommender system is being
proposed and a prototype is developed under this project. Every human user of the
recommender system is represented through an agent. The information is distributed over
the entire network in the form of information with the agents. The agents of the
application domain form a trust based virtual community referred to as “web of trust”.
The architecture provides ways to quantify degree of trust between the agents initially
when the system starts and also when a new agent comes into the application domain.
Trust is initialized to zero in the beginning or can be subjectively given by the
administrator of the system to give good recommendations, which is then updated on the
basis of interactions between the agents.
All recommender agents have their own knowledge about the varieties in specific Zone
stored in their own location. Hence, the recommender system being developed under A
RECOMMENDER SYSTEM-AFCA uses the decentralized database. A user agent asks
for recommendations from various recommender agents in its neighborhood, which in
turn can take recommendations from their trustworthy neighbors. The user agent then
prioritizes the received recommendations based on trust on recommenders and generates
a ranked list of recommendations for the user.
The prototype A RECOMMENDER SYSTEM-AFCA has two different types of agents:
1. User Agent (UA)
2. Recommender Agent (RA)
3.5.1 User Agent (UA): User sends the request to the UA. UA collects the data from the user using web based
GUI. It captures the information for the attributes to be considered for selection of a
variety and their degree of significance. UA also stores trust values for each interacting
recommender agent known to it and prioritizes the recommender agents according to
their trust value.
33
Temperature Spacing Plant Type Disease Resistance Ingredients
.7845 .6873 .5974 .2345 .2323
Table 1: Degree of significance of attributes of varieties
Recommenders R1 R2 R3 R4 R5
Degree of Trust 0.78 0.68 0.85 0.23 0.76
Table 2: Degree of Trust of user on the recommenders
Once, UA receives the recommendations from all trustworthy recommender agents in its
neighborhood, it generates the final recommendation list of varieties after taking into
account the degree of trust on each of the recommender agent. UA also updates the
degree of trust on each recommender agent after evaluating the recommendations given
by them.
3.5.2 The Recommender Agent (RA): The Recommender agent (RA) after getting a request from user agent generates a set of
recommendations in order to best satisfy the incoming request. Every recommendation
corresponds to a variety for a specific crop for a given Zone is in the form of
Intuitionistic fuzzy Set (IFS). The IFS recommendation for a variety has a degree of
membership (satisfaction), degree of non-membership (dissatisfaction) and the degree of
hesitation (uncertainty) signifying the relevance, irrelevance and uncertainty of the
variety for a given user. To personalize the variety recommendations according to the
taste of the user agent A, the recommender agent maintains the following lists:
• Preference list: The preference list, PA consists of the information in terms of the
attributes (Temperature, spacing, plant type, disease resistance, Ingredients etc.) of
the varieties liked by the user in the past connected to user agent A. There are
separate sublists in PA corresponding to the attributes Temperature, spacing, plant
type, disease resistance, Ingredients. The order of names of varieties in a sublist in PA
34
corresponding to a particular attribute signifies their priority in their respective
sublists.
Preferences in the sublists ( PA )
Temperature V3 V5 V2 V1 V4
Spacing V1 V2 V4 V3 V5
Plant Type V2 V5 V4 V1 V3
Disease Resistance V1 V3 V2 V5 V4
Ingredients V5 V4 V3 V1 V2
Table-3: Preference List (all the sublists) about taste of user maintained by recommenders
• Uncertain list: The uncertain list, UA consists of the same type of information as that
of the preference list. However, it is unordered list accumulated during unintentional
encounters and the recommender agent has no idea whether the user prefers one
variety over the other.
Uncertain List (UA )
V6 V8 V7 V10 V9
Table-4: Uncertain List (UA) about taste of user maintained by recommenders
In this research work, we are trying to build a prototype for a system similar to a social
recommendation process. The system is also capable of recommending a variety to a user
if the variety has a general appeal. In such cases, if the user likes the variety that actually
does not conform to his/her tastes explicitly mentioned, the user agent gives a feedback to
the recommender agent(s) who recommended that particular variety. The recommender
agent on getting the feedback from the user agent adds the variety in the preference list
for that user.
35
3.6 Algorithms used in proposed Recommender System:
3.6.1 Generating Recommendations
Various ways in which a recommender agent recommends a variety are:
• Case 1: The variety is in the preference list corresponding to the user stored in
the recommender agent.
• Case 2: The recommender agent comes to know about the variety through a
trustworthy acquaintance during unintentional encounters.
• Case 3: The recommender has used the variety, and feels that the variety has a
general appeal even if it does not conform to the tastes of the user. Recommender
agent recommends that variety to the user with the degree of uncertainty. The
degree of uncertainty signifies the extent to which the recommender is not sure
about his/her decision to suggest that variety to the user. The degree of
membership is zero for such varieties and the third parameter is computed using
the other two degrees. All such varieties are recommended to the user.
The varieties of case 3 are recommended whether they are according to the tastes of the
user or not. For the varieties of case 1 and case 2, matching is done with PA and UA and
based on the matching results, the recommendation is generated.
3.6.1.1 IFS generation for Varieties
A recommender can recommend varieties known to it. The recommender agent comes to
know about the variety either through usage or through unintentional encounters. During
the unintentional encounters, an agent exchanges the information about only those
varieties that it has used and is satisfied with.
An agent stores the names of the experienced varieties. When an agent has to generate
recommendations for other agent, it retrieves knowledge about experienced varieties.
Let a variety V be represented by n attributes (a1, a2… an). A variety V is suggested to the
user agent A, along with the IFS generated for it as shown below:
36
1. The degree of membership of variety V, μv is computed using the preference list PA, as: 1.1 For every attribute ai (i = 1, …, n) of variety V, do the following:
1.1.1 Search the position pi of variety V in preference list PA then compute the
rank Ranki as the normalized position of V in the range [0 to 1] using the
following formula:.
Ranki = 1- ((pi – 1) / (max-1))
where
max represents total number of varieties exist in PA
1.1.2 Finally, degree of membership of variety V, μv is computed as:
μv = n
rankdan
iii∑
=1)*(
where dai (i = 1, 2, …, n) represents the normalized degree of significance that
the user associates with the ith attribute,
n represents the total number of attributes of variety V in PA.
2. The degree of uncertainty of variety V, πv is computed using the uncertainty list UA,
created during the unintentional encounter among the trustworthy agents and on the
experienced varieties of the recommenders that are not matched with varieties of PA
as:
2.1 πv is computed from PA, using the experienced varieties list by assigning the
satisfaction values of V, greater than some threshold value.
2.2 πv is computed using the uncertainty list UA, as:
2.2.1 Let ki be the attribute value of V for attribute ai in UA.
2.2.2 Compute the degree of uncertainty of the V as:
n) k da ... k da k (da nn22 11 ∗++∗+∗
=vπ
where, dai (i = 1, 2, …, n) represents the degree of significance that the user associates with the ith attribute.
n represents the number of varieties in UA. The degree of non-membership of variety V, νv is computed as follows:
vv - - 1 πμν =v
37
3.6.1.2 Recommendation List Generation by Recommender Agent After matching the varieties with the preference list and uncertain list, the degree of
membership, degree of non-membership and uncertainty is available with the
recommender agent for all the varieties that it knows. The following method is used to
generate the final list of the varieties that are to be recommended to the user agent along
with IFS that is computed for them:
1. All the varieties that are part of case 3, section 3.6.1 are to be considered for
further processing.
2. For all the varieties of case 1 and 2, section 3.6.1, do the following:
2.1 The varieties with non-zero degree of membership are followed by the
varieties with non-zero degree of uncertainty.
2.2 Within the varieties with non-zero degree of membership, order the
varieties in descending order on degree of membership.
2.3 Within the varieties with non-zero degree of uncertainty, order the
varieties in descending order on degree of uncertainty.
3.6.2 Final Recommendation List Generation by User Agent
The user agent needs to form an aggregated list out of the IFS recommendations lists
received from the trustworthy recommender agents. User agent has to generate a final
consolidated list from all the recommendations that are received from the
recommenders. The user agent computes the degree of importance of a variety on the
basis of degree of trust on the recommenders who have recommended the variety, the
relative position of the variety in the list of recommenders and the IFS
recommendation of the recommender. The user agent generates a final consolidated
list from all the recommendations that are received from the recommenders using the
following aggregation method:
1. First identify the distinct varieties from the lists and then compute the degree of
importance (DoI) of every variety (Vi) as follows: ( ) ( ) ( ) ( ) ( )
( ) ( ){ }( ) ( ) ( ) ( ){ } ( )))(
)),......()()((),}{((
22222
11111
kikikikik
iiii
iiii
RRankRRRRDoTABSRRankRRRRDoTABS
RRankRRRRDoTABSMAXDoI
∗∗−∗∗∗−∗
∗∗−∗=
πνμπνμ
πνμ
38
where,
DoIi(A) is degree of importance of Vi as computed by A,
Rj is the jth recommender,
μi(X) is the degree of membership of Vi according to recommender X,
νi(X) is the degree of non-membership of Vi according to recommender X,
πi(X) is degree of uncertainty or hesitation of Vi according to recommender X,
DoT(Rj) is the degree of trust of the A on Rj,
Ranki(Rj) is the normalized position of Vi in the recommendation list of Rj,
k is the total number of recommenders who have recommended Vi.
2. Compute the threshold, TDOI for degree of importance as
TDOI = μ – ν * π
where,
μ, ν and π are degree of membership, non membership and uncertainty,
respectively that the user agent expects from the interesting variety.
3. For all the distinct varieties, Vi of step 1, the degree of importance obtained is a
real number that lies between 0 and 1; and hence there is no need for its
normalization. Arrange the products in the descending order of their degrees of
importance.
3.6.3 Initializing and Updating Degree of Trust on the Recommenders
3.6.3.1 Trust Initialization When a new agent comes up in the system or the system starts from the scratch, then the
agents have to initialize the trust values for some of the other agents in the application
domain to form its acquaintance set. If an agent is known to the other agent (i.e., the
corresponding humans know each other), then the human associated with the agent can
initialize the degree of trust according to the personal dealings with the person. However,
the system also allows an agent to initialize degree of trust on an agent X, on the basis of
the experiences of the other agents with X, i.e., to what extent the other agents in the
39
application domain have received good recommendations from X. The degree of trust is
then regularly updated on the basis of the personal experience of the agent with X.
The new agent Y, asks for the experience of known agents’ (connected to it) w.r.t. X. Let
q agents return their experience values as the number of good recommendations received
to the total number of the recommendations received from X. Let jth agent gives the
experience as ej. Then the degree of trust on X is as following:
q
eXDoT
q
jj∑
== 1)(
where, DoT(X) is degree of trust as computed by Y on X.
If q is large, then basically we are interested in finding what is the experience of
majority of the agents? Then experiences can then be clustered and degree of trust can be
computed [Kaur et al., 2005].
3.6.3.2 Trust Updation The degree of trust on a recommender is updated on the basis of the distance between
degree of importance of the variety as it is there in the aggregated list of the user agent
(A) and the recommendation list of the recommender (R). The difference of opinion
between the user and the recommender is computed as follows: ( )
p D ... D D
d P21 +++=
where, Di = {μi(R) – νi(R) * πi(R)} – {μ – ν * π},
μ, ν, π; and μi(R), νi(R), πi(R) are as defined earlier.
p is the total number of varieties in the recommendation list of R.
Depending upon whether the difference between its aggregated list and the
recommendations is below its acceptable threshold dt or not, the user agent updates the
degree of trust, DoT(R) on recommender as follows:
DoT(R) = DoT(R) + (dt – d) In our model, hence trust increases for those who give good recommendations and vice-versa.
40
Chapter 4
System Architecture of Recommender System 4.1 Introduction:- In the present context, there are number of domain experts (Farmers) each having
knowledge of their domain according to their own perspective. Now, if the user wants
information about the use of strategies and set of varieties that can be deployed in a
specific Zone such as Uttar Pradesh, Himachal Pradesh, he may contact the domain
experts who provide their knowledge of the specific problem area. Thereafter accordingly
the user processes the information gathered according to his specification. As the experts
may be geographically distributed, this knowledge engineering problem is a distributed
computing problem. Research literature on intelligent agent system architectures has
established that problems that are inherently distributed can be efficiently implemented as
a multi agent system. So the prototype proposes the solution based on a multi-agent
system approach, where every user in the prototype is represented by an agent. The
agents communicate with others to generate the recommendation using various
techniques like Content Based, Collaborative and Hybrid approach.
The prototype “A Recommender System for Crops & Varieties Selection-A farmer
Centered Approach” addresses the influence of Zone over varieties system operates. The
prototype integrates the knowledge of various domain experts (farmers) thereby
providing a solution to its users by recommending a set of varieties that is useful and can
be used for a particular Zone. This in turn expands the user knowledge regarding the
effects of Zones.
Using this Recommender System a user can get the recommendation on the use of
variety/ set of varieties in a user specified Zone from the available set of varieties, it takes
into account the influence of Zone over which farmers and varieties systems operate
thereby expanding the body of knowledge regarding the effects of Zone. The prototype
41
combines several domain experts’ knowledge to compute the recommendations. Thus, in
order to cover a certain range of tactical engagements, a recommendation for a set of
varieties will be obtained.
4.2 System Architecture:-
4.2.1 Program Structure Program structure defines the boundaries of the system within which the system will be
developed. This head provides pictorial view of the system which helps in understanding
various modules and sub-system of the system. Information like how the system interacts
with the user, how the input given by the user produces the output, what are the various
processes available to the user, type of users that are available with the system, etc.
4.2.2 Architecture Diagram The overall system would be presented shown pictorially with Block diagram, DFD and
ERD of the proposed system.
Block Diagram
Figure 4, shows the interaction of the user with the prototype. User provides the Zone
description as input and the system in turn produces the recommended ranked variety list
under crops from which the user finally selects a variety(s) for use. The system comprises
of certain crop which contain number of varieties of similar attributes.
Zone Description Variety list as
recommendation
Figure 4: Block Diagram
RECOMMENDER SYSTEM FOR
CROPS. & VARIETIES SELECTION – A
FARMER CENTERED APPROACH
42
4.2.3 Data Flow Diagram
The data flow diagram as shown in figure 5 describes how the user interacts with the
system and the process flow of the system.
Figure 5: Data Flow Diagram
43
4.2.4 Entity Relationship Diagram Figure 6 shows the entity relationship diagram for the prototype under consideration.
Figure 6: Entity - Relationship Diagram
44
4.3 Data Design 4.3.1 Database Description
The database for the proposed system has the following relations, in which some of them
are static and some are dynamically created. The purpose and attributes of each of these
tables is explained in Annexure I.
Zone Info
Variety Info Field Name Type Keys Description
Variety_ID VARCHAR2(15) Primary Key
Variety_Name VARCHAR2(40)
Description VARCHAR2(80)
Crop Info Field Name Type Keys Description
Crop_ID VARCHAR2(15) Primary Key
Crop _Name VARCHAR2(20)
Description VARCHAR2(80)
Field Name Type Keys Description
Zone_ID VARCHAR2(15) Primary Key
Zone_Name VARCHAR2(20)
Description VARCHAR2(80)
45
Zone_Crop Info Field Name Type Keys Description
Zone_Crop_ID VARCHAR2(15) Primary Key
Zone_ID VARCHAR2(15)
Foreign Key on Zone_ID of
Zone Info.
Crop_ID VARCHAR2(15) Foreign Key on Crop_ID of
Crop Info.
(Zone_ID and Crop_ID) must be a candidate Key.
Attribute Info Field Name Type Keys Description
Attribute_ID VARCHAR2(15) Primary Key
Attribute_Name VARCHAR2(20)
Description VARCHAR2(80)
Crop_Attribute Info Field Name Type Keys Description
Crop_Attribute_ID VARCHAR2(15) Primary Key
Crop _ID VARCHAR2(15) Foreign Key on Crop _ID of Crop
Info.
Attribute_ID VARCHAR2(15) Foreign Key on Attribute_ID of
Attribute Info.
(Crop _ID and Attribute_ID) must be a candidate Key.
46
User Account Info Field Name Type Keys Description
User_ID VARCHAR2(15) Primary Key
User_Type VARCHAR2(20)
Description VARCHAR2(80)
User_Name VARCHAR2(20)
Password VARCHAR2(15)
Address VARCHAR2(50)
Phone_No NUMBER(10)
Designation VARCHAR2(20)
Crop_ Variety Info Field Name Type Keys Description
Crop _Variety_ID VARCHAR2(15) Primary Key
Variety_ Crop _ID VARCHAR2(15) Foreign Key on
Variety_ Crop _ID of
Crop Info.
Variety_ID VARCHAR2(15) Foreign Key on Variety-
_ID of Variety Info.
(Variety_ Crop _ID and Variety_ID) must be a Candidate key.
Attribute_Variety Info Field Name Type Keys Description
Attribute_ID VARCHAR2(15) .
Variety_ID VARCHAR2(15)
Value NUMBER(6,6)
(Attribute_ID, Variety_ID) must be a Candidate Key.
47
Ajay_Trust
Field Name Type Keys Description
User_Name VARCHAR2(15) .
Trust_On VARCHAR2(15)
Trust_Value NUMBER(6,6)
(User_Name, Trust_On and Trust_Value) must be a Candidate Key.
Ajay_Significance
Field Name Type Keys Description
Attribute_ID VARCHAR2(20) Primary Key
Significance_Value NUMBER(20,18)
Gaurav_Uncertain
Field Name Type Keys Description
Variety_ID VARCHAR2(20)
Gaurav_Final_Recommendation
Field Name Type Keys Description
Variety_ID VARCHAR2(15)
D_O_Imp NUMBER(8,6)
Ui_Vi_Pii NUMBER(8,6)
48
Gaurav_Variety
Field Name Type Keys Description
Variety_ID VARCHAR2(20) Primary Key
Satisfaction_Rate NUMBER(8,6)
4.4 Implementation Details
A Recommender System for Crops & Varieties Selection - A Farmer Centered Approach
is designed and developed using JADE (Java Agent Development Environment), JSP and
Oracle 9i. An experiment is conducted in which 10 domain experts (farmers) were asked
to help the users decide about which variety to use in a specific zone. The dataset is
collected from the IASRI for generating the recommendations and trust updation. This
dataset contained the details of 10 zones, 12 crops, 15 varieties under each crop category
and 6 variety attributes.
The AFCA system starts with 10 recommender agents (domain experts), who have their
own knowledge about the varieties in specific zone stored in their own location. User
agent creates when user logs into the system. The user agent has a certain degree of trust
on these recommender agents, which is represented in the Table-2. In Table -1, degrees
of significance that the user agent associates with the attributes of a variety. The Table –
3 gives the preference list that the user agent gave to the recommender agents. The
information in the Table – 4 is what the recommender agents know, during the
unintentional encounters.
User agent receives the query regarding variety recommendation from the user and passes
this query to its trustworthy recommender agents. The recommender agents respond with
the degrees of membership and non-membership about the varieties. Finally the
aggregated list of all the recommendations as computed by the user agent and given to
user.
49
The following snapshots give the implementation details of the AFCA system and which
will guide the user how to interact with the system.
4.4.1 Login
This is first screen that displays. It allows the user to access different screens based upon
the user’s role. Administrator, Domain expert and User are different roles assigned to
user. User with administrator role has to login first for successfully starting the
application.
Fig.7 Login window of AFCA
4.4.2 Domain Expert Agents
Once administrator successfully logs into the system then all the domain expert agents
run automatically. These agents were created in the JADE (Java Agent Development
Environment). Fig.8. shows agent environment in which domain expert agents are
running.
50
Fig.8 Agent Environment
4.4.3 Unintentional Encounters
The recommender agent comes to know about the variety through unintentional
encounters. During the unintentional encounters, an agent exchanges the information
about only those varieties that it has used and is satisfied with. Fig.9 shows the
information exchange among the agents during unintentional encounters.
Fig.9 Information exchange among the agents during unintentional encounters
51
4.4.4 Recommender’s known varieties details
For creating a known variety list user selects the variety from the existing variety list to
known variety list shown in Fig.10 then user fills the satisfaction in percentage for the
experienced varieties shown in Fig 11.
Fig.10 Selection of user’s known variety list
Fig.11 User’s known variety details
52
5.2.5 Recommender’s experienced variety details
The system allows the recommenders to share the experienced varieties knowledge.
Recommender selects the appropriate zone and crop under which he experienced the
varieties then he selects the list of experienced varieties from the pool of existing list
shown in Fig-12. Fig 13 shows the recommender ranks the list of selected varieties
according to variety attributes.
Fig.12 Selection of experienced varieties
Fig.13. Ranking of varieties according to the variety attributes
53
5.2.6 Update trust
Trust of the user on recommenders is updated by the system implicitly based on
importance of recommendation for the user. System also allows the user to explicitly
update the trust on the recommenders shown by Fig 14.
Fig. 14 Updation of trust of the user over recommenders
5.2.7 Recommendation Generation
System captures the information of degree of significance for the variety attributes shown
in Fig 15 then system allows the user to select the zone over which variety has to use
shown in Fig.16. After that system generates the recommendation list for the specified
zone shown by Fig.17.
Fig.15 Grade the degree of significance
54
Fig.16 Selection of zone for generating the recommendation
Fig.17 Recommendation Report
55
Chapter 5
Conclusion and Future Work
The research work “Recommender System for Crops & Varieties Selection -A Farmer
Centered Approach” addresses the influence of Zone over which farmers and variety
systems operate. The prototype integrates the knowledge of various domain experts
thereby providing a solution to its users by recommending a set of variety systems that
are useful and can be used for a particular Zone. This in turn expands the user knowledge
regarding the effects of Zone.
All existing recommender systems employ one or more of a handful of basic techniques:
content-based, collaborative, hybrid. A survey of these techniques shows that they have
complementary advantages and disadvantages. This fact has provided incentive for
research in hybrid recommender systems that combine techniques for improved
performance.
Using this Recommender System a user can get the recommendation on the use of variety
/ set of varieties in a user specified Zone from the available set of varieties, it takes into
account the influence of Zone over which farmers operate thereby expanding the body of
knowledge regarding the effects of Zone. The prototype combines several domain
experts’ knowledge to compute the recommendations. Thus, in order to cover a certain
range of tactical engagements, a recommendation for a set of varieties will be obtained
The existing recommender systems base recommendations on similarity between the user
and the recommenders or between the items. In the first case, the user profile is matched
with the database of profiles to find the similar profiles. The products preferred by those
similar people are suggested to the user also. In the second type of recommender systems,
the database is mined to find products that are normally preferred together. Depending
upon what user has already purchased/shown preference for; the other products that go
along with it are suggested to the user. However, the studies have shown that the users
56
prefer recommendations from friends as compared to the recommendations received from
these recommender systems. This is because the existing recommender systems work like
a black box and hence it is difficult for the user to accept the recommendations of the
system.
To overcome this problem of lack of trust on the recommendation systems this model
incorporates the social recommendation process. The trustworthy peers of the user
become the recommender agents and suggest varieties to the user according to the tastes
of the user. The agents in our system also learn from their experience in dealing with the
trustworthy peers and update the degree of trust on them. In this system, we have tried to
merge the advantages of the mechanical recommender system with the more human
recommendation process to make their recommendations trustworthy and useful for the
user.
Future versions of the present system can incorporate neural models of adversarial
strategies while computing recommendations.
57
Annexure I
a) Database Table Description
• User This table stores the details of the users, authorized to use this system.
Name Data type Description Remarks User_Id Varchar(20) Every user has a unique
user id, used for authentication
Primary Key
Password Char(10) It stores password given by user
-
User_name Varchar(20) It stores the name of the user
-
Address Varchar(20) It stores the address of user
-
City Varchar(20) It stores the city of user - State Varchar(20) It stores the state of user - Country Varchar(20) It stores the country of
user -
Phone Number(10) It stores the phone number of user
-
Institute_id Varchar(20) It stores the id of institute where the user works
Foreign Key-(Institute(Institute_id))
Usertype_id Varchar(20) It sorer the type of user Foreign Key-(UserType(Usertype_id))
• UserType
This table stores the different types of users permitted to uses the system:
Name Data type Description Remarks Usertype_Id Varchar(20) Every type of user has a
unique user type id Primary Key
Usertype Varchar(10) It stores the type of user - Usertype_desc Varchar(20) It stores the description
about that user type -
58
• Institute This table stores the details of the institute where the user belongs to.
Name Data type Description Remarks Institute_Id Varchar(20) Every institute has a
unique institute_id. Primary Key
Institute_Acro Char(10) It stores the acronym of the institure
-
Institute_name Varchar(20) It stores the name of the institute
-
Institute_Address Varchar(20) It stores the address of the institute
-
Institute_City Varchar(20) It stores the city of institute
-
Institute_State Varchar(20) It stores the state of institute
-
Institute_Country Varchar(20) It stores the country where the institute is located
-
Institute_Phone Number(10) It stores the phone number of the institute
-
Institute_fax Varchar(20) It stores the fax number of the institute
Foreign Key-(Institute(Institute_id))
Institute_website Varchar(20) It stores the website address of the institute
Institute_Email It stores the Email Id
• Proj_Basic_Data: This table contains the data regarding the project
Name Data type Description Remarks Proj_Code Varchar(20) Every project title is given
an unique id Primary Key
TiTle VarChar(200) The title of the project is stored here
-
Proj_Start_Date Date It stores the date when the project was started
-
Proj_End_Date Date It stores the date when the project came to an end
-
Institute_Id Varchar(20) It stores the city of user Refers Institute(Institute_id)
PI_Name Varchar(20) It stores the name of PI of that project
Refers Users(User_Id)
Status Char(10) The staus “new”,”comp”, “ongoing” is stored
59
• Proj_Objective This table stores the Objectives corresponding to
each Project Title Name Data type Description Remarks Proj_objective_Id Varchar(20) Every Objective is
given an unique Id Primary Key
Pcode Varchar(20) The project Titlecode is given here
Refers Proj_Basic_Data(Proj_Cde)
Proj_Objective Varchar(200) The objective statement is given here
-
• Proj_Activity The descriptions of all the activities are mentioned in this table
Name Data type Description Remarks Proj_Actv_Code Varchar(20) Every Activity
is given an unique Id
Primary Key
Proj_Obj_Id Varchar(20) The Id of the Objective to which the activity belongs to is mentioned
Refers Proj_Objective(Proj_Objective_Id)
Proj_Activity Varchar(200) The activity statement is given here
-
60
Annexure II
RiTa.WordNet Libraries
Fields RiWordNet.ADJ String constant for Adjective part-of-speech
RiWordNet.ADV String constant for Adverb part-of-speech
RiWordNet.NOUN String constant for Noun part-of-speech
RiWordNet.VERB String constant for Verb part-of-speech
Methods exists() Checks the existence of a ‘word’ in the ontology
getAllAlsoSees()
Returns also-see terms for all senses ofword/pos or null if not found Holds for nouns (?) & adjectives Example: happy -> [cheerful, elated, euphoric, felicitous, joyful, joyous...]
getAllAntonyms()
Returns String[] of Antonyms for the 1st sense of word with pos or null if not found Holds for adjectives only (?)
References
61
getAllCoordinates()
Returns coordinate terms for all sense of word/pos, or null if not found X is a coordinate term of Y if there exists a term Z which is the hypernym of both X and Y. Examples:
• blackbird and robin are coordinate terms (since they are both a kind of thrush)
• gun and bow are coordinate terms (since they areboth varieties)
• fork and spoon are coordinate terms (since they are both cutlery, or eating utensils)
• hat and helmet are coordinate terms (since they are both a kind of headgear or headdress)
Example: arm -> [hind-limb, forelimb, flipper, leg, crus, thigh, arm...] Holds btwn nouns/nouns and verbs/verbs
getAllDerivedTerms()
Returns derived terms forall senses of word/pos or null ifnot found Holds for adverbs Example: happily -> [jubilant, blithe, gay, mirthful, merry, happy]
getAllExamples()
Returns examples for all senses of word with pos if they contain the word, else null if not found
getAllGlosses()
Returns glosses for all senses of ‘word’ with ‘pos’, or null if not found
getAllHolonyms()
Returns part-to-whole relationships for all sense of word/pos, or none if not found X is a meronym of Y if Y has X as a part. X is a holonym of Y if X has Y as a part. That is, if Y is a meronym of X. Holds between: nouns and nouns Returns part, member, and substance holonyms Example: arm -> [body, physical-structure, man, human...]
getAllHypernyms()
Returns an ordered String[] of hypernym-synsets (each a semi-colon delimited String) up to the root of WordNet for the 1st sense of the word, or null if not found
getAllHyponyms()
Returns an unordered String[] of hyponym-synsets (each a colon-delimited String), or null if not found
References
62
getAllMeronyms()
Returns array of whole-to-part relationships for all senses of word/pos, or null if not found X is a meronym of Y if Y has X as a part. X is a holonym of Y if X has Y as a part. That is, if Y is a meronym of X. Holds between: Nouns and nouns Returns part,member, and substance meronyms Example: arm -> [wrist, carpus, wrist-joint, radiocarpal-joint...]
getAllNominalizations()
Returns nominalized terms for all sense of word/pos or null if not found Refers to the use of a verb or an adjective as a noun. Holds for nouns, verbs & adjecstives(?) Example: happiness(n) -> [happy, unhappy] happy(a) -> [happiness, felicity]
getAllSimilar()
Returns similar-to list for all sense of word/pos or null if not found Holds for adjectives Example: happy(a) -> [blessed, blissful, bright, golden, halcyon, prosperous...]
getAllSynonyms()
Returns an unordered String[] containing the synset, hyponyms, similars, alsoSees, and coordinate terms (checking each in order) for all senses of word with pos, or null if not found
getAllSynsets()
Returns String[] of words in each synset for all senses of word with pos, or null if not found
getAllVerbGroups()
Returns verb group for all senses of verb or null if not found Example: live -> [dwell, inhabit] Holds for verbs
getAlsoSees()
Returns also-see terms for 1st sense of word/pos or null if not found Holds for nouns (?) & adjectives Example: happy -> [cheerful, elated, euphoric, felicitous, joyful, joyous...]
getAnagrams() Returns up to maxResults full anagram matches for the specified word and pos
References
63
Example: ‘table’ returns ‘bleat’ (but not ‘tale’).
getAntonyms()
Returns String[] of Antonyms for the 1st sense of word with pos or null if not found Holds for adjectives only (?)
getAnyExample()
Return a random example from the set of examples from all senses of word with pos, assuming they contain word, or else null if not found
getBestPos()
Finds the most-common part-of-speech for the word, according to its polysemy count, returning the pos for the version of the word with the most different senses.
getCommonParent()
Returns common parent for words with unique ids id1, id2, or null if either word or no parent is found
getCommonParents()
Returns String[] of Common Parents for 1st senses of words with specified pos’ or null if not found
getContains()
Returns up to maxResults of the specified pos where each contains the given word
Example: ‘table’ returns ‘bleat’ (but not ‘tale’).
getCoordinates()
Returns coordinate terms for 1st sense of word/pos, or null if not found X is a coordinate term of Y if there exists a term Z which is the hypernym of both X and Y. Examples:
• blackbird and robin are coordinate terms (since they are both a kind of thrush)
• gun and bow are coordinate terms (since they are both varieties)
• fork and spoon are coordinate terms (since they are both cutlery, or eating utensils)
• hat and helmet are coordinate terms (since they are both a kind of headgear or headdress)
Example: arm -> [hind-limb, forelimb, flipper, leg, crus, thigh, arm...] Holds btwn nouns/nouns and verbs/verbs
getDerivedTerms() Returns derived terms for 1st sense of word/pos or null if not found
References
64
Holds for adverbs Example: happily -> [jubilant, blithe, gay, mirthful, merry, happy]
getDescription()
Returns description for word with pos or null if not found
getDistance()
Returns the min distance between any two senses for the 2 words in the WordNet tree (result normalized to 0-1) with specified pos, or 1.0 if either is not found
getEndsWith()
Returns up to maxResults of the specified pos ending with the given word.
Example: ‘table’ returns ‘turntable’ & ‘uncomfortable’
getExamples()
Returns examples for word with unique senseId, or null if not found
getGloss()
Returns full gloss for !st sense of ‘word’ with ‘pos’ or null if not found
getHolonyms()
Returns part-to-whole relationships for 1st sense of word/pos, or none if not found X is a meronym of Y if Y has X as a part. X is a holonym of Y if X has Y as a part. That is, if Y is a meronym of X. Holds between: nouns and nouns Returns part, member, and substance holonyms Example: arm -> [body, physical-structure, man, human...]
getHypernyms()
Returns Hypernym String[] for all senses of word with pos or null if not found
X is a hyponym of Y if there exists an is-a relationship between X and Y. That is, if X is a subtype of Y. Or, for xample, if X is a species of the genus Y. X is a hypernym of Y is Y is a hyponym of X. Holds between: nouns and nouns & verbs and verbs Examples:
• artifact is a hyponym of object • object is a hypernym of artifact • carrot is a hyponym of herb
References
65
• herb is a hypernym of carrot
getHypernymTree()
Returns an ordered String[] of hypernym-synsets (each a semi-colon delimited String) up to the root of WordNet for the id, or null if not found
getHyponyms()
Returns Hyponym String[] for 1st sense of word with pos or null if not found
X is a hyponym of Y if there exists an is-a relationship between X and Y. That is, if X is a subtype of Y. Or, for xample, if X is a species of the genus Y. X is a hypernym of Y is Y is a hyponym of X. Holds between: nouns and nouns & verbs and verbs Examples:
• artifact is a hyponym of object • object is a hypernym of artifact • carrot is a hyponym of herb • herb is a hypernym of carrot
getHyponymTree()
Returns an unordered String[] of hyponym-synsets (each a colon-delimited String) representing all paths to leaves in the ontology (the full hyponym tree), or null if not found
getMeronyms()
Returns array of whole-to-part relationships for 1st sense of word/pos, or null if not found X is a meronym of Y if Y has X as a part. X is a holonym of Y if X has Y as a part. That is, if Y is a meronym of X. Holds between: Nouns and nouns Returns part,member, and substance meronyms Example: arm -> [wrist, carpus, wrist-joint, radiocarpal-joint...]
getNominalizations()
Returns nominalized terms for 1st sense of word/pos or null if not found Refers to the use of a verb or an adjective as a noun. Holds for nouns, verbs & adjecstives(?)
References
66
Example: happiness(n) -> [happy, unhappy] happy(a) -> [happiness, felicity]
getPos()
Returns an array of all parts-of-speech ordered according to their polysemy count, returning the pos with the most different senses in the first position, etc.
getRandomExample() Returns a random example from a random word w’ pos
getRandomExamples()
Returns numExamples random examples from random words w’ pos
getRandomWord()
Returns a random word with pos and a maximum of maxChars.
getRandomWords() Returns count random words w’ pos
getRegexMatch()
Returns up to maxResults of the specified pos matching the the given regular expression pattern.
getSenseCount()
Return the # of senses (polysemy) for a given word/pos. A ‘sense’ refers to a specific WordNet meaning and maps 1-1 to the concept of synsets. Each ‘sense’ of a word exists in a different synset.
For more info, see: {@link http://WordNet.princeton.edu/gloss}
getSenseIds()
Returns String[] of unique ids, one for each ‘sense’ of word with pos, or null if none are found.
A WordNet ‘sense’ refers to a specific WordNet meaning and maps 1-1 to the concept of synsets. Each ‘sense’ of a word exists in a different synset.
For more info, see: {@link http://WordNet.princeton.edu/gloss}
getSimilar()
Returns similar-to list for first sense of word/pos or null if not found Holds for adjectives Example: happy(a) -> [blessed, blissful, bright, golden, halcyon, prosperous...]
References
67
getSoundsLike()
Returns up to maxResults of the specified pos that match the soundex code of the given word.
getStartsWith()
Returns up to maxResults of the specified pos starting with the given word.
Example: ‘turn’ returns ‘turntable’
getStems() Returns an array of all stems, or null if not found
getSynonyms()
Returns an unordered String[] containing the synset, hyponyms, similars, alsoSees, and coordinate terms (checking each in order) for all senses of word with pos, or null if not found
getSynset()
Returns String[] of words in synset for first sense of word with pos, or null if not found.
getVerbGroup()
Returns verb group for 1st sense of verb or null if not found Example: live -> [dwell, inhabit] Holds for verbs
getWildcardMatch()
Returns up to maxResults of the specified pos matching a wildcard pattern, with * ‘*’ equals any number of characters, and ‘?’ equals any single character.
Example : ‘t ?le’ returns (tale,tile,tole) Example : ‘t*le’ returns (tatumble, turtle, tussle, etc.) Example: ‘t?le*’ returns (telex, tile,tilefish,tile,talent, tiles, etc.)
ignoreCompoundWords()
ignoreUpperCaseWords()
isAdjective()
isAdverb()
isIgnoringCompoundWords()
References
68
isIgnoringUpperCaseWords()
isNoun()
isStem()
Returns true if ‘word’ exists with ‘pos’ and is equal (via String.equals()) to any of its stem forms, else false;
isVerb()
printHypernymTree()
Prints the full hypernym tree to System.out (primarily for debugging).
printHyponymTree() Prints the full hyponym tree to System.out (primarily for debugging).
References
69
References Adomavicius G., and Tuzhilin A., 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. In IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, pp. 734-749. Atanassov, K., 1999. Intuitionistic Fuzzy Sets: Theory and Applications. Studies in Fuzziness and Soft Computing, 35, Physica-Verlag. Baldi P., Frasconi P. and Smyth P., 2003. Commerce on the Web: Models and Applications. Modeling the Internet and the Web, Probabilistic Methods and Algorithms, John Wiley & Sons Ltd., West Sussex, England, pp. 211-234. Bedi P. and Kaur H., 2006. Trust based Personalized Recommender System. In INFOCOM Journal of Computer Science, Vol. 5, N.1, pp. 19-26. Bonhard P., 2004. Improving Recommender Systems with Social Networking. In the Proceedings Addendum of the 2004 ACM Conference on Computer-Supported Cooperative Work. Chicago, IL, USA. Breese J., Heckerman D. and Kadie C., 1998. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In the Proceedings of Fourth Annual Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, Madison, WI, USA, pp. 43-52.
Burke R., 2002. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, Vol. 12, pp. 331-370. Herlocker J.L., Konstan J.A., Terveen L.G. and Riedl J., 2004. Evaluating Collaborative Filtering Recommendations. In ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 5-53. Huang Z., Chen H. and Zeng D., 2004a. Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering. In the ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 116-142. Li Q. and Kim B.M., 2003. Clustering Approach for Hybrid Recommender System. In the Proceedings of the IEEE/WIE International Conference on Web Intelligence (WI’03). Resnick P. and Varian H.R., 1997. Recommender Systems. In the Communications of the ACM, Vol. 40, Vol. 3, pp. 56-58.
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Sarwar B., Karypis G., Konstan J. and Riedl J., 2000b. Analysis of Recommender Algorithms for E-Commerce. In the Proceedings of Electronic Commerce, Minneapolis, Minnesota, USA, pp. 158-167.
Sarwar B., Karypis G., Konstan J., and Riedl J., 2001. Item-Based Collaborative Filtering Recommendation Algorithms. In the Proceedings of World Wide Web, Hong Kong, pp. 285-295 Schafer J.B., Konstan J. and Riedl J., 1999. Recommender Systems in E-Commerce. In the Proceedings of First ACM Conference on Electronic Commerce, Denver, USA. Schafer J.B., Konstan J. and Riedl J., 2002. Meta-Recommender Systems: User-controlled Integration of Diverse Recommendations. In the Proceedings of Conference on Information and Knowledge Management (CIKM ’02), McLean, Virginia, USA, pp. 43-51. Semeraro G., Lops P. and Dedemmis M., 2005. WordNet-based User Profiles for Neighborhood Formation in Hybrid Recommender Systems. In the Proceedings of the 5th International Conference on Hybrid Intelligent Systems (HIS '05). Shardanand U., and Maes P., 1995. Social information filtering: Algorithms for Automating “Word of Mouth”. In the Proceedings of the ACM CHI’95 Conference on Human Factors in Computing Systems, Denver, CO, pp. 210-217. Sinha R. and Swearingen K., 2001. Comparing Recommendation made by Online Systems and Friends. In the Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries, Ireland.
Ungar L.H. and Foster D.P., 1998. Clustering Methods for Collaborative Filtering. In the Proceedings of the Workshop on Recommender Systems, AAAI Press.
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Annexture I
Abbreviations Terms
AFCA A Farmer Centered Approach
JADE Java Agent Development Environment
GUI Graphical User Interface
HTML Hyper Text Markup Language
JSP Java Server Pages
