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The goal of a recommender system is to generate meaningful recommendations to a collection of users for items or products that might interest them. Many of the largest e-commerce websites are already using recommender systems to help their customers find products to purchase or download.
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GENETIC ALGORITHM
BASED MUSIC
RECOMMENDER .
(GAMR)
INTRODUCTION Users are usually looking for items
they find interesting
Website is a collection of these items
Huge amounts of data available
We propose a system using a
combination of conventional
techniques and genetic algorithm
Used by E.commerce site
AIMS AND OBJECTIVES
Generate meaningful recommendations
Prompt responses and adaptation to
changing preferences
High recommendation accuracy
Enriched user interface
WHAT IS RECOMMENDATION SYSTEM
Internet-based software tools
Provides user with intelligent suggestions
Recommender systems for music data produce a list of
recommendations
Content-based filtering
Collaborative filtering
Based on information and characteristics of the items
CONTENT-BASED
FILTERING
PLAN OF ACTION (Item profile+User profile+Prediction mechanism
Hip-hopKanye westRihanna…
recommend items with
similar content build
match
User profile
recommend
likes
Item profile
Good LifeE.TRun This TownGold Digger
Predict items based on the items previously rated by other
similar users
Recommended items that are preferred by other people
Example of a collaborative filtering technique.
COLLABORATIVE
FILTERING
ABCD
A BCDE
ABCDJ
A BCDE
ABDE
ABCDE
UserDatabase
CorrelationMatch
ABCD
ActiveUser
ABC:E
ExtractRecommendations E
E
LITERATURE SURVEYED Existing Systems Proposed system
Focus on accessed items only Considers all items available in database
Not prompt to immediate changes in user interest
IGA prompts to immediate changes in user preferences
Unable to learn from user actions and implement them
Adapts to user actions to compute accordingly
Accuracy is not great The offspring generated are quite optimal
GENERIC RS
For a typical recommender system, there are three
steps
1. User provides some form of input to the
system.
2. These inputs are brought together to form a
representation of the users likes and dislikes.
3. System computes recommendations
GENETIC ALGORITHM
A genetic algorithm (GA) is a search heuristic that mimics the
process of natural evolution
Genetic algorithms belong to the larger class of evolutionary
algorithms (EA), which generate solutions to optimization
problems
Use techniques inspired by natural evolution, such as
replication, inheritance, mutation, selection, and crossover
GENETIC ALGORITHM PROCEDURE
1. Choose the initial population of individuals
2. Valuate the fitness of each individual
3. Repeat until termination
4. Select the best-fit individuals for reproduction
5. Breed new individuals through crossover and mutation
6. Evaluate the individual fitness of new individuals
7. Replace least-fit population with new individuals
FLOW CHART OF SYSTEM
SYSTEM ANALYSIS
The proposed system is divided into three phases, namely,
1. Music Feature Extraction
2. Evaluation
3. Interactive Genetic Algorithm
In our proposed system, IGA works in three steps:
Selection,Crossover, and Matching.
SYSTEM ARCHITECTURE
RESULT AND DISCUSSION
SCOPE OF THE SYSTEM More than half the music now-a-days is downloaded
The trend is bound to rise exponentially
Virtually impossible to go through the heap of data and
choose
Recommendations from primary sources are too narrow
They amount to a bulk of online sales across sectors
These systems are attracting huge attention and
investments from e-commerce sites
TECHNICAL REQUIREMENTS
HARDWARE :
256 MB RAM
80 GB HDD
Intel 1.66 GHz Processor Pentium 4
SOFTWARE :
Visual Studio 2008(.Net framework)
MS SQL Server 2005
CONCLUSION
We propose a real-time genetic
recommendation method for music data in
order to overcome the shortfalls of existing
recommendation systems based on content based
filtering and other such techniques that fail in
reflecting in the current user preferences.
REFERENCES[1] Hyun – Tae Kim, Eungyeong Kim, “Recommender
system based on genetic algorithm for music data”, 2nd International Conference on Computer Engineering and Technology, 2010.
[2] J. Ben Schafer, Joseph Konstan, John Riedl,
“Recommender Systems in ECommerce”,2007. [3]Sachin Bojewar and Jaya Fulekar , “Application of
Genetic Algorithm For Audio Search with Recommender System”, 2006.
[4] Tom V. Mathew, “Genetic algorithm”,2005.
THANK YOU
