Session Aware Music Recommendation System with Matrix Factorization technique-SVD

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-30 Number-4
Year of Publication : 2015
Authors : M. Sunitha, Dr. T. Adilakshmi
  10.14445/22312803/IJCTT-V30P131

MLA

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
[1] M. Sunitha , Dr. T. Adilakshmi, Session Aware Music Recommendation System with User-based and Item-based Collaborative Filtering Method, International Journal of Computer Applications, June ,2014
[2] M. Sunitha Reddy, Dr. T. Adilakshmi, User Based Collaborative Filtering For Music Recommendation System, International Journal of Innovative Research and Development, Dec 2013, Volume 2, Issue 12 pg no 185-190
[3] M.Sunitha Reddy ,Dr. T. Adilakshmi, Music Recommendation System based on Matrix Factorization technique –SVD, International Conference on Computer Communications and Informatics (ICCCI-14), Coimbatore, 3-5 January, 2014
[4] Context-aware item-to-item recommendation within the factorization framework, Balázs Hidasi, Domonkos Tikk, CaRR’13, February 5, 2013, Rome, Italy
[5] Introduction to Recommender Systems, Markus Zanker, Dietmar Jannach, Tutorial at ACM Symposium on Applied Computing 2010 ,Sierre, Switzerland, 22 March 2010
[6] A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, SongJie Gong, Journal Of Software, Vol. 5, No. 7, July 2010
[7] NetflixPrize, http://www.netflixprize.com/, 2012.
[8] The Million Song Dataset Challenge, Brian McFee, Thierry Bertin-Mahieux, Daniel P.W. Ellis, Gert R.G. Lanckriet, WWW 2012 Companion, April 16–20, 2012, Lyon, France
[9] Adomavicius, G., Tuzhilin, A. 2005. Toward the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactionson Knowledge and Data Engineering 17, 734– 749.
[10] Singular Value Decomposition http://en.wikipedia.org/wiki/Singular_value_decompositio n

Keywords
Collaborative filtering, recommender system, Item-based clusters ,user-based clusters, Matrix factorization technique, SVD.