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![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.](https://image.slidesharecdn.com/gamrpptwww-140206001822-phpapp01/85/genetic-algorithm-based-music-recommender-system-22-320.jpg)
Uploaded byneha pevekar
genetic algorithm based music recommender system
The document presents a genetic algorithm-based music recommender system (GAMR) that aims to generate meaningful recommendations and adapt to users' changing preferences. It combines conventional techniques with a genetic algorithm to improve recommendation accuracy and user interface engagement. The proposed system addresses the limitations of existing recommendation systems that often fail to reflect current user interests.
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genetic algorithm based music recommender system
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- 2. INTRODUCTION Users are usuallylooking 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 RECOMMENDATIONSYSTEM 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 oninformation 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 itemsbased on the items previously rated by other similar users Recommended items that are preferred by other people Example of a collaborative filtering technique.
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- 9. LITERATURE SURVEYED Existing Systems Proposedsystem 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 atypical 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 geneticalgorithm (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. Choosethe 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 OFSYSTEM
- 14. SYSTEM ANALYSIS The proposedsystem 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.
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- 19. SCOPE OF THESYSTEM 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 : 256MB 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 areal-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.
Editor's Notes
- #10 We proposed a new recommender system for music data by combining the content based filtering technique with interactive genetic algorithmthe content based approach has some limitation such tha it focuses on only the accessed items and is not prompt to immediate changes in the potential interest of users.to overcome these limitations,we combine the content based filtering approach and genetic algorithm in our prposedsystGAs produces offspring(i.e;new solutions) by the combination of selected solutionswe proposed real-time genetic recommendation method in order to overcome the existing recommendation techniques are notreflect the current user`s intend. With the genetic algorithm newer solutions can be generatedproviding optimal solution each time when the algorithm is made to run, thus providingmutations. This method can be compared with the existing ones which lack the quality of providing accurate results.
- #11 For a typical recommender system, there are three stepsThe user provides some form of input to the system. These inputs can be both explicit and implicit . Ratings submitted by users are among explicit inputs whereas the URLs visited by a user and time spent reading a web site are among possible implicit inputs.These inputs are brought together to form a representation of the users likes and dislikes. This representation could be as simple as a matrix of items-ratings, or as complex as a data structure combining both content and rating information.The system computes recommendations using these user profiles.
- #17 WORKING:Apply genetic algorithm to music recommendation system. The system can detect and recommend appropriate songs which are suitable for user’s musical preference. And, the system requires pre-processing which is feature (i,e. tempo, chord, pitch, etc.) extraction from music. It based on shuffle operation. (i.e, play song randomly) At first time, the system recommends songs randomly and user cans judge’s song’s preference by controlling their devices or program. Just click the next song button.
- #18 Description: This is the general list where all thesongs of different category is listed. The user canlisten to songs as well as rate them. Once userprovides ratings, user is directed to the recommendedlist of songs based on user preference
- #19 Description: This is the final output page of therecommender system. This page displays the top tenrecommendations to the user based o his preferences.