Personalized Information Retrieval system using Computational Intelligence Techniques

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Objectives ofResearchWork

Introduction

LiteratureSurvey

ProposedResearchWorks

TermAssociationGraph Model forDocumentRe-ranking

Topic Model forDocumentRe-ranking

GeneticIntelligenceModel forDocumentRe-ranking

SwarmIntelligenceModel for SearchQueryReformulation

Conclusion

References

Publications

Personalized Information Retrieval SystemUsing Computational Intelligence Techniques

VENINGSTON K

Senior Research FellowDepartment of Computer Science and EngineeringGovernment College of Technology, Coimbatore

[email protected]

Under the Guidance ofDr.R.SHANMUGALAKSHMI

Associate Professor, Dept. of CSE, GCT

05 August 2015

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Presentation Outline

1 Objectives of Research Work

2 Introduction

3 Literature Survey

4 Proposed Research WorksTerm Association Graph Model for Document Re-rankingTopic Model for Document Re-rankingGenetic Intelligence Model for Document Re-rankingSwarm Intelligence Model for Search Query Reformulation

5 Conclusion

6 References

7 Publications


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Objectives of Research Work

To improve the retrieval effectiveness by employing TermAssociation Graph data structure

To enhance a personalized ranking criteria by modeling ofuser’s search interests as topics. Further, employingDocument topic model that integrates User topic model

To realize Genetic Algorithm enabled document re-rankingscheme

To devise personalized search query suggestion using AntColony Optimization


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Introduction [1/2]

Typical Information Retrieval (IR) Architecture


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Introduction [2/2]

Why Personalization in Information Retrieval?


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Classifications of Typical IR systems

Content-based approach

Simple matching of a query with results - This does not helpusers to determine which results are worth

Author-relevancy technique

Citation and hyperlinks - Presents the problem of authoringbias i.e. results that are valued by authors are not necessarilythose valued by the entire population

Usage rank approach

Actions of users to compute relevancy - Computed from thefrequency, recency, duration of interaction by users


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Limitations in Typical IR systems

Most of the techniques measure relevance as a function ofthe entire population of users

This does not acknowledge that relevance is relative foreach user

There needs to be a way to take into account thatdifferent people find different things relevant

User’s interests and knowledge change over time -personal relevance


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General Approach for mitigating Challenges

Main ways to personalize a search are Result processingand Query augmentation

Document Re-ranking

To re-rank the results based upon the frequency, recency, orduration of usage. Provides users with the ability to identifythe most popular, faddish pages that other users have seen

Query Reformulation

To compare the entered query against the contextualinformation available to determine if the query can berefined/reformulated to include other text


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General Problem Description

Diverse interest of search usersOriginal Query User 1 User 2 User 3

World cup football championship ICC cricket world cup T20 cricket world cup

India crisis Economic crisis in India security crisis in India job crisis in India

Job search Student part time jobs government jobs Engineering and IT job search

Cancer astrology and zodiac lung cancer and prevention causes of cancer, symptoms and treatment

Ring Ornament horror movie circus ring show

Okapi animal giraffe African luxury hand bags Information retrieval model BM25


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Literature Survey

Related work on Re-ranking techniquesPaper Title Author, Year Techniques used Limitations

Implicit preference Sugiyama et al, 2004 Term frequency scheme Noisy browsing history

Hyperlink data Brin & Page, 1998 Link structure analysis Computes universal notion of importance

Collaborative filtering Sarwar et al, 2000 Groupization algorithm User data are dynamic

Categorization Liu et al, 2004 Mapping queries to related categories Predefined categories are used

Long-term user behavior Bennett et al, 2012 Create profiles from entire history Misses searcher needs for the current task

Short-term user behavior Cao et al, 2008 Create profiles from recent search session Lacks in capturing users long term interest

Location awareness Leung et al, 2010 Location ontology Captures location information by text matching

Task awareness Luxenburger et al, 2008 Task language model Lacks temporal features of user tasks

Tag data Carman et al, 2008 Content based profiles Results are biased towards particular user group

Context data White et al, 2009 User modeling uses Contextual features Treat all context sources equally

Click data Liu et al, 2002 Assesses pages frequently clicked Makes no use of terms and its association


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Literature Survey

Related work on Query reformulation techniquesPaper Title Author, Year Techniques used Limitations

Anchor text Kraft & Zien, 2004 Mining anchor texts More number of query suggestions

Bipartite graph Mei et al, 2008 Prepares morphological different keywords Individual user intents are not considered

Personalized facets Koren et al, 2008 Employs key-value pair meta-data Uses frequency based facet ranking

Term association pattern Wang & Zhai, 2008 Analyze relations of terms inside a query Click-through data not considered

Rule based classifier Huang & Efthimiadis, 2009 Matches query with ordered reformulation rules Semantic associations are missing

Clustering Jain & Mishne, 2010 Query suggestions are grouped by topics Drift in user intent to another topic

Merging Sheldon et al, 2011 Produces results from different reformulations Random walk on the click graph


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Proposed Research Works

Module 1

Term Association Graph Model for Document Re-ranking

Module 2

Topic Model for Document Re-ranking

Module 3

Genetic Intelligence Model for Document Re-ranking

Module 4

Swarm Intelligence Model for Search Query Reformulation


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1. Term Association Graph Model for DocumentRe-ranking

Problem Statement

How to represent document collection as term graphmodel?

How to use it for improving search results?

Methodology

Term graph representation

Ranking semantic association for Re-ranking

TermRank based approach (TRA)Path Traversal based approach (PTA)

1 PTA1: Naive approach2 PTA2: Paired similarity document ordering3 PTA3: Personalized path selection


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Document Representation

Sample of OHSUMED (Oregon Health & ScienceUniversity MEDline) test Collection

DocID Item-set Support

54711 Ribonuclease, catalytic, lysine, phosphate, enzymatic, ethylation 0.12

55199 Ribonuclease, Adx, glucocorticoids, chymotrypsin, mRNA 0.2

62920 Ribonuclease, anticodon, alanine, tRNA 0.1

64711 Cl- channels, catalytic, Monophosphate, cells 0.072

65118 isozyme, enzyme, aldehyde, catalytic 0.096

Supportd =

∑ni=1 fd(ti )∑N

j=1

∑ni=1 fd(ti )


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Term Association Graph Model


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Ranking Schemes based on Semantic Association


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Term Rank Approach (TRA)

Rank(ta) = c∑

tb∈Ta

Rank(tb)Ntb

ta and tb are Nodes

Tb is a set of terms ta points to

Ta is a set of terms that point to ta

Ntb = |Tb| is the number of links from ta


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PTA1: Naive Approach

The sequence of documents are chosen from path p3 i.e.D11,D1,D37,D17,D22, andD5. D11 will be the top rankeddocument.


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PTA2: Paired Similarity Ranking

sim(T1,T2) = 2 ∗ depth(LCS)depth(T1)+depth(T2)

T1 and T2 denote the term nodes in Term AssociationGraph TG

LCS denote the Least Common Sub-Sumer of T1 and T2

depth(T ) denote the shortest distance from query node qto a node T on TG


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PTA3: Personalized Path Selection


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PSCweight =1

|t|

[[#topics∑i=1

(sivi (∑

t ∈ Ti ))

]∗[

1− #t /∈ T

|t|

]]

PSCweight is the Personalized Search Context Weight

|t| is the total number of terms in dfs–path includingquery term

T is the set of user interested topics

sivi is the search interest value of i th topic


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Experimental Dataset


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Evaluation Measures

Subjective Evaluation1 Information Richness

InfoRich(Rm) =1

Div(Rm)

Div(Rm∑k=1

1

Nk

Nk∑i=1

InfoRich(d ik)

Objective Evaluation1 Precision P = #RelevantRetrived

k

2 Recall P = #RelevantRetrievedTotal#Relevant

3 Mean Average Precision MAP =∑|Q|

q=1 AvgPrecision(q)

|Q|

AvgPrecision(q) = 1R

∑Rk=1 ((P@k) . (rel (k)))

4 Mean Reciprocal Rank MRR = 1|Q|∑|Q|

i=11

ranki5 Normalized Discounted Cumulative Gain

NDCGk =∑k

i=12ri−1

log2(i+1)

IDCGk


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Experimental Results & Analysis [1/3]

Non-Personalized Evaluation on Real Dataset


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Non-Personalized Evaluation on Synthetic Dataset


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Personalized Evaluation on Real Dataset


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Motivation to Module 2

Summary of Module 1

1 Employs termassociation graphmodel

2 Suggested differentmethods to enhancethe documentre-ranking

3 Captures hiddensemantic association

Exploits topical representationfor identifying user interest.Matching of documents andqueries is not done with topicalrepresentation. To explore topicmodel to find relevantdocuments by matching topicalfeatures


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2. Topic Model for Document Re-ranking

Problem Statement

How to model and represent past search contexts?

How to use it for improving search results?

Methodology

User search context modeling1 User profile modeling2 Learning user interested topic3 Finding document topic

Personalized Re-ranking process1 Exploiting user interest profile model2 Computing personalized score for document using user

model3 Generating personalized result set by re-ranking


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User search context modeling

User profile modeling

θu = UPwi

UPwi∈History(D) = P(wi ) =tfwi ,D∑

wi∈D tfwi ,D


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User search context modeling

Learning user interested topic

Finding document topic


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Personalized Re-ranking process

Exploiting user interest profile model

KLD(Td ‖ Tu) =∑

t∈D∩UP(Td(t))log

P(Td(t))

P(Tu(t))


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Personalized Re-ranking process

Computing personalized score for document using usermodel

P(D | Q, θu) =(D | θu)P(Q | D, θu)

P(Q | θu)

P(Q | D, θu) = P(Q | Td ,Tu)+∏qi∈Q

(βP(qi | θu)+(1−β)P(qi | D))

P(Q | Tu,Td) =∏qi∈Q

(αP(qi | Tu) + (1− α)P(qi | Td))

Generating personalized result set by re-ranking

1 The documents are scored based on P(Q | D, θu)2 Result set is re-arranged based on descending order of the

personalized score


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Parameter Tuning

Learning α and β Parameters


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Evaluation on Real Dataset


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Evaluation on AOL Dataset


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Summary of Module 2

1 Client sidepersonalization

2 Insensitive to thenumber of Topics

3 Not all the querieswould requirepersonalization to beperformed

Explores topic model for findingrelevant documents usingtopical features. To learn atopic model on a representativesubset of a collection usingGenetic Intelligence technique


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3. Genetic Intelligence Model for DocumentRe-ranking

Problem Statement

How to represent documents as chromosomes?

How to evaluate fitness of search results?

Methodology

Apply GA with an adaptation of probabilistic model

Probabilistic similarity function has been used for fitnessevaluation

Documents are assigned a score based on the probabilityof relevance

Probability of relevance are sought using GA approach inorder to optimize the search process i.e. finding of relevantdocument not by assessing the entire corpus or collection


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Why GA for IR?

When the document search space represents a highdimensional space i.e. the size of the document corpus ismultitude in IR

GA is the searching mechanisms known for its quick searchcapabilities

When no relevant documents are retrieved in top orderwith the initial query

The probabilistic exploration induced by GA allows theexploration of new areas in the document space.


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Steps in GA for IR


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Fitness Evaluation

Representations of Chromosomes

Probabilistic Fitness Functions

1 P(q | d) =∑

w∈d(P(q | w)P(w | d))

2 P(q | d) = αP(q | C ) + (1− α)∑

w∈d(P(q | w)P(w | d))


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Selection & Reproduction

Roulette-wheel selection

Reproduction Operators1 Crossover2 Mutation


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Parameter Tuning

Learning Pc and Pm Parameters


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Evaluation on Benchmark Dataset


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Evaluation on Real Dataset


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Summary of Module 3

1 Explored the utility ofincorporating GA toimprove re-ranking

2 Adaptation ofpersonalization in GAprovides more desirableresults

3 Not all the querieswould requirepersonalization to beperformed

The graph representation ofdocuments best suit theapplication of SwarmIntelligence model. To simulateACO in graph structure basedon behavior of ants seeking apath between their colony andsource of food for search queryreformulation


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4. Swarm Intelligence Model for Search QueryReformulation

Problem Statement

How to address vocabulary mismatch problem in IR?

How to change the original query to form a new querythat would find better relevant documents?

Methodology

Exploits Ant Colony Optimization (ACO) approach tosuggest related key words

The self-organizing principles which allow the highlycoordinated behavior of real ants that collaborate to solvecomputational problems


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ACO for Query Reformulation

Terminologies

Artificial Ant

Pheromone

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ACO Implementation


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Characteristics of Artificial Ant

Notion of autocatalytic behavior

Chooses the query term to go with a transition probabilityas a function of the similarity i.e. amount of trail presenton the connecting edge between terms

Navigation over retrieved documents for a query is treatedas ant movement over graph

When the user completes a tour, a substance called trail ortrace or pheromone is laid on each edge


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Similarity

Transition Probability

pkij (t) =[τij(t)]α[ηij ]

β∑k [τik(t)]α[ηik ]β

ηij is a static similarity weight

τij is a trace deposited by users

Trail Deposition

τij(t + 1) = p ∗ τij(t) + ∆τij

p is the rate of trail decay per time interval i.e. pheromoneevaporation factor

∆τij is the sum of deposited trails by users


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AOL Search query log

Only the queries issued by at least 10 users were employedand the pre-processed documents retrieved for that querywere used to construct graph

270 single and two word queries issued by different usersfrom AOL search log are taken


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Experimental Evaluation

Baseline Methods

Association Rule based approach (AR)

SimRank Approach (SR)

Backward Random Walk approach (BRW)

Forward Random Walk approach (FRW)

Traditional ACO based approach (TACO)

Parameter setting

Depth was set as 5 i.e. top ranked 5 related queries

Evaporation factor (p) was set to 0.5


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Experimental Results & Analysis

Manual Evaluation

Benchmark Evaluation


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Summary

Summary of Module 4

1 Terms in the initial set of documents constitute potentialrelated terms

2 Semantically related keywords are suggested to the initialquery

3 Single word queries are treated as an ambiguous one


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Conclusion

1 Usage of Term Association GraphEfficient retrieval of Journal articlesThe graph structure may signify grammatical relationsbetween terms

2 Integration of Document Topic model and User Topicmodel

Effective in general searchThis may incorporate live user feedback

3 GA based document fitness evaluationGood in document space explorationChromosomes representation may be improved

4 ACO based query reformulationExploits collaborative knowledge of usersIf the solution is badly chosen, the probability of a badperformance is high


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Further Extension

1 Efficient updating policy for user interest models

2 Account individual user specific context for generatingquery refinements

3 Medical Information Retrieval (Eg. PubMed, WebMD,etc.)


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Book References

Salton & McGill (1986)

Introduction to modern information retrieval

McGraw-Hill , New York.

Baeza-Yates & Ribeiro-Neto (1999)

Modern Information Retrieval

Addison Wesley .

Manning et al (2008)

Introduction to Information Retrieval

Cambridge University Press .

Goldberg (1989)

Genetic Algorithms in Search, Optimization, and Machine Learning

Addison-Wesley .


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References [1/7]

Matthijis & Radlinski (2012)

Personalizing Web Search using Long Term Browsing History

In Proc. 4th ACM WSDM , 25 – 34.

Agichtein et al (2006)

Improving Web Search Ranking by Incorporating user behaviorinformation

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Ponte & Croft (1998)

A language modeling approach to information retrieval

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Lafferty et al (2001)

Document language models, query models, and risk minimization forinformation retrieval


Kushchu (2005)

Web-Based Evolutionary and Adaptive Information Retrieval

IEEE Trans. Evolutionary Computation 9(2), 117 – 125.


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Leung & Lee (2010)

Deriving Concept-based User profiles from Search Engine Logs

IEEE Trans. Knowledge and Data Engineering 22(7), 969 – 982.

Blanco & Lioma (2012)

Graph-based term weighting for information retrieval

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Dorigo et al (2006)

Ant Colony Optimization

IEEE Computational Intelligence Magazine 1(4), 28 – 39.

Sugiyama et al (2004)

Adaptive web search based on user pro?le constructed without anyeffort from users

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Brin Page (1998)

The Anatomy of a Large-Scale Hypertextual Web Search Engine

Elsevier Journal on Computer Networks and ISDN Systems 30(1-7),107 – 117.


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Eirinaki Vazirgiannis (2005)

UPR:Usage-based page ranking for web persoanalization

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Bennett et al (2012)

Modeling the impact of short- and long-term behavior on searchpersonalization


cao et al (2008)

Context-Aware Query Suggestion by Mining Click-Through

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References [4/7]

carman et al (2008)

Tag data and personalized Information Retrieval

In Proc. ACM workshop Search in social media , 27 – 34.

Leung et al (2010)

Personalized Web Search with Location Preferences

In Proc. 26th IEEE Intl. Conf. Data Engineering , 701 – 712.

Luxenburger et al (2008)

Task-aware search personalization

In Proc. 31st ACM SIGIR , 721 – 722.

white et al (2009)

Predicting user interests from contextual information

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White et al (2013)

Enhancing personalized search by mining and modeling task behavior

In Proc. 22nd Intl. Conf. World Wide Web , 1411 – 1420.


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Vallet et al (2010)

Personalizing web search with Folksonomy-Based user and documentprofiles

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Jansen et al (2007)

Determining the user intent of web search engine queries

In Proc. 6th Intl. Conf. World Wide Web, 1149 – 1150.

Jansen et al (2000)

Real life, real users, and real needs: a study and analysis of userqueries on the web

Elsevier Information Processing and Management 36(2), 207 – 227.

Daoud et al (2008)

Learning user interests for a session-based personalized Search

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Kraft Zien (2004)

Mining Anchor Text for Query Refinement

In Proc. 13th Intl. Conf. World Wide Web , 666 – 674.VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 65 / 71

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References [6/7]

Dang Croft (2010)

Query Reformulation Using Anchor Text

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Mei et al (2008)

Query suggestion using hitting time

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Koren et al (2008)

Personalized interactive faceted search


Wang Zhai (2005)

Mining term association patterns from search logs for effective queryReformulation

In Proc.ACM CIKM , 479 – 488.

Huang Efthimiadis (2009)

Analyzing and evaluating query reformulation strategies in web searchlogs

In Proc. 18th ACM CIKM , 77 – 86.VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 66 / 71

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Jain Mishne (2010)

Organizing query completions for web search

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Sadikov et al (2010)

Clustering query refinements by user intent


Bhatia (2011)

Query suggestions in the absence of query logs

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Sheldon et al (2011)

LambdaMerge: Merging the results of query reformulations

In Proc. 4th ACM WSDM, 117 – 125.

Goyal et al (2012)

Query representation through lexical association for informationretrieval



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Journal Publications

Veningston, & Shanmugalakshmi (2015)

Semantic Association Ranking Schemes for Information RetrievalApplications using Term Association Graph Representation

Sadhana - Academy Proceedings in Engineering Sciences , SpringerPublication. [Annexure I]

Veningston & Shanmugalakshmi (2014)

Computational Intelligence for Information Retrieval using GeneticAlgorithm

INFORMATION - An International Interdisciplinary Journal 17(8),3825 – 3832. [Annexure I]


Combining User Interested Topic and Document Topic forPersonalized Information Retrieval

Lecture Notes in Computer Science Springer Publication 8883 , 60 –79.[Annexure II]


Efficient Implementation of Web Search Query reformulation usingAnt Colony Optimization

Lecture Notes in Computer Science Springer Publication 8883 , 80 –94.[Annexure II]


Ph.D.Viva-Voce

VENINGSTONK


Introduction

LiteratureSurvey






Conclusion

References

Publications

International Conference Publications [1/2]

Veningston, & Shanmugalakshmi (2015)

Personalized Location aware Recommendation System

In Proc. 2nd IEEE Intl. Conf. Advanced Computing andCommunication Systems , Indexed in IEEE Xplore. [Best Paper]


Information Retrieval by Document Re-ranking using TermAssociation Graph

In Proc. ACM Intl. Conf. Interdisciplinary Advances in AppliedComputing , Indexed in ACM Digital Library. [Best Paper]


Personalized Grouping of User Search Histories for Efficient WebSearch

In Proc. 13th WSEAS Intl. Conf. Applied Computer and AppliedComputational Science , 164 – 172.


Statistical language modeling for personalizing Information Retrieval

In Proc. 1st IEEE Intl. Conf. Advanced Computing andCommunication Systems , Indexed in IEEE Xplore. [Best Paper]

Veningston, Shanmugalakshmi & Ruksana (2013)

Context aware Personalization for Web Information Retrieval A Largescale probabilistic approach

In Proc. Intl. Conf. Cloud and Big Data Analytics , PSG College ofTechnology.


Enhancing personalized web search Re-ranking algorithm byincorporating user profile

In Proc. 3rd IEEE Intl. Conf. Computing, Communication andNetworking Technologies , Indexed in IEEE Xplore.


Ph.D.Viva-Voce

VENINGSTONK


Introduction

LiteratureSurvey






Conclusion

References

Publications

International Conference Publications [2/2]

Veningston, Shanmugalakshmi & Ruksana (2013)

Context aware Personalization for Web Information Retrieval: A Largescale probabilistic approach

In Proc. Intl. Conf. Cloud and Big Data Analytics , PSG College ofTechnology.


Enhancing personalized web search Re-ranking algorithm byincorporating user profile

In Proc. 3rd IEEE Intl. Conf. Computing, Communication andNetworking Technologies , Indexed in IEEE Xplore.


Ph.D.Viva-Voce

VENINGSTONK


Introduction

LiteratureSurvey






Conclusion

References

Publications

Thank You & Queries


Data & Analytics

Personalized Information Retrieval system using Computational Intelligence Techniques