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Session Aware Music Recommendation System with User-based and Item-based Collaborative Filtering Method

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 24
Year of Publication: 2014
M. Sunitha
T. Adilakshmi

M Sunitha and T Adilakshmi. Article: Session Aware Music Recommendation System with User-based and Item-based Collaborative Filtering Method. International Journal of Computer Applications 96(24):22-27, June 2014. Full text available. BibTeX

	author = {M. Sunitha and T. Adilakshmi},
	title = {Article: Session Aware Music Recommendation System with User-based and Item-based Collaborative Filtering Method},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {24},
	pages = {22-27},
	month = {June},
	note = {Full text available}


Recommender systems have been proven to be valuable means for web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. The recommendations provided are aimed at supporting their users in various decision making process, such as what items to buy. In Music Recommendation System, we recommend items to users based on their interest. First we use collaborative filtering method to identify the items which are similar and similarity among users based on the users listening history. Proposed Algorithm recommend the items to new users based on the item clusters and user clusters formed. Later we have taken timestamp of user logs also into consideration to form Sessions. Finally we have evaluated the performance of the proposed algorithm with sessions and with -out sessions . Our experiment show that the accuracy of recommendation system with sessions outperformed the conventional user-based & item-based collaborative filtering method.


  • Breese J, Hecherman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI'98). 1998. 43-52.
  • Chong-Ben Huang, Song-Jie Gong, Employing rough set theory to alleviate the sparsity issue inrecommender system, In: Proceeding of the Seventh International Conference on Machine Learning and Cybernetics (ICMLC2008), IEEE Press, 2008, pp. 1610-1614.
  • Sarwar B, Karypis G, Konstan J, Riedl J. Item- Based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International World Wide Web Conference. 2001. 285-295.
  • Manos Papagelis, Dimitris Plexousakis, Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents, Engineering Application of Artificial Intelligence 18 (2005) 781-789.
  • Hyung Jun Ahn, A new similarity measure for collaborative filtering to alleviate the new user cold- starting problem, Information Sciences 178 (2008) 37-51.
  • SongJie Gong, The Collaborative Filtering Recommendation Based on Similar-Priority and Fuzzy Clustering, In: Proceeding of 2008 Workshop on Power Electronics and Intelligent Transportation System (PEITS2008), IEEE Computer Society Press, 2008, pp. 248-251.
  • SongJie Gong, GuangHua Cheng, Mining User Interest Change for Improving Collaborative Filtering, In:Second International Symposium on Intelligent Information Technology Application(IITA2008), IEEE Computer Society Press, 2008, Volume3, pp. 24-27.
  • Duen-Ren Liu, Ya-Yueh Shih, Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences, The Journal of Systems and Software 77 (2005) 181–191.
  • Million Song Dataset, of?cial website by Thierry Bertin-Mahieux, available at: http://labrosa. ee. columbia. edu/millionsong/.
  • 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