Session Aware Music Recommendation System with Matrix Factorization technique-SVD
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2015 by IJCTT Journal|
|Year of Publication : 2015|
|Authors : M. Sunitha, Dr. T. Adilakshmi|
|DOI : 10.14445/22312803/IJCTT-V30P131|
M. Sunitha,Dr. T. Adilakshmi "Session Aware Music Recommendation System with Matrix Factorization technique-SVD". International Journal of Computer Trends and Technology (IJCTT) V30(4):174-181, December 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Recommender systems (RS) serve as valuable information filtering tools for web online users to deal with huge amount of information available on the Internet. RS can be used in making decision in various fields like which books to purchase or which music to listen and so on. In this paper we have proposed and implemented an algorithm based on the Collaborative filtering method and Matrix Factorization technique -SVD. Collaborative filtering is one of the traditional method for Recommendation Systems based on the user feedback. Matrix factorization is a method to address the problem of Sparsity. In this paper , first sessions are formed based on the timestamps of user logs. Collaborative filtering is used to form clusters based on users and items. SVD is applied for the user-item matrix formed from the clusters to address the Sparsity problem. Finally recommendations are given to the new test users by using user and item clusters. Experiments are performed on the benchmark data set for the proposed algorithm and results shows improvement of the recommendation system accuracy over traditional collaborative filtering method.
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Collaborative filtering, recommender system, Item-based clusters ,user-based clusters, Matrix factorization technique, SVD.