Published on February 6, 2014
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 Prompt meaningful recommendations 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
CONTENT-BASED FILTERING Based on information and characteristics of the items
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
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.
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
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.
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  Hyun – Tae Kim, Eungyeong Kim, “Recommender system based on genetic algorithm for music data”, 2nd International Conference on Computer Engineering and Technology, 2010.  J. Ben Schafer, Joseph Konstan, John Riedl, “Recommender Systems in ECommerce”,2007. Sachin Bojewar and Jaya Fulekar , “Application of Genetic Algorithm For Audio Search with Recommender System”, 2006.  Tom V. Mathew, “Genetic algorithm”,2005.
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