Session Aware Music Recommendation System with Matrix Factorization technique-SVD
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2015 by IJCTT Journal | ||
| Volume-30 Number-4 |
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| 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.
Abstract -
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.
References
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n
Keywords
Collaborative filtering, recommender
system, Item-based clusters ,user-based clusters,
Matrix factorization technique, SVD.