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.

1. GENETIC ALGORITHM
BASED
MUSIC RECOMMENDER .
(GAMR)

2. 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

3. AIMS AND OBJECTIVES
 Generate
 Prompt
meaningful recommendations
responses and adaptation to changing
preferences
 High
recommendation accuracy
 Enriched
user interface

4. 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

5. CONTENT-BASED FILTERING

Based on information and characteristics of the items

6. PLAN OF ACTION (Item profile+User
profile+Prediction mechanism
Item profile
likes
recommend items with
similar content
build
recommend
Good Life
E.T
Run This Town
Gold Digger
match
Hip-hop
Kanye west
Rihanna…
User profile

7. COLLABORATIVE FILTERING

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.

8. User
Database
A
B
C
D
A
B
C
D
E
A
B
C
D
J
A
B
C
D
E
Correlation
Match
Active
User
A
B
C
D
A
B
D
E
A
B
C
D
E
A
B
C
:
E
Extract
Recommendations
E
E

9. 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

10. 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

11. 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

12. 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

13. FLOW CHART OF SYSTEM

14. 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.

16. SYSTEM ARCHITECTURE

17. RESULT AND DISCUSSION

19. 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

20. 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

21. 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.

22. 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.