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Title

Real Time Recommender System for Music Data

Author

Manjula Athani Neelam Pathak Asif Ullah Khan Bhupesh Gour

Citation

Vol. 15  No. 8  pp. 88-91

Abstract

Recommender system is able to identifying the n-number of users preferences and adaptively recommend music tracks according to user preferences. we are extracting unique feature tempo of each music using Myrsyas Tool. Then we are applying BLX- α crossover to a extracted feature of each music track. User favorite and user profiles are included. This system have been emerging as a powerful technique of e-commerce. The majority of existing recommender systems uses an overall rating value on items for evaluating user’s preference opinions. Because users might express their opinions based on some specific features of the item, recommender systems could produce recommendations that meet user needs. In this paper we presented a Real time recommender system for music data. Multiuser Real time recommender system combines the two methodologies, the content based filtering technique and the interactive genetic algorithm by providing optimized solution every time and which is based on user’s preferences We can also share the favorite songs to other user hence it give better result and better user system.

Keywords

Recommender system, Interactive Genetic algorithm, Content Based filtering BLX- α

URL

http://paper.ijcsns.org/07_book/201508/20150817.pdf