Advanced SearchSearch Tips
A Music Recommendation Method Using Emotional States by Contextual Information
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Music Recommendation Method Using Emotional States by Contextual Information
Kim, Dong-Joo; Lim, Kwon-Mook;
  PDF(new window)
User`s selection of music is largely influenced by private tastes as well as emotional states, and it is the unconsciousness projection of user`s emotion. Therefore, we think user`s emotional states to be music itself. In this paper, we try to grasp user`s emotional states from music selected by users at a specific context, and we analyze the correlation between its context and user`s emotional state. To get emotional states out of music, the proposed method extracts emotional words as the representative of music from lyrics of user-selected music through morphological analysis, and learns weights of linear classifier for each emotional features of extracted words. Regularities learned by classifier are utilized to calculate predictive weights of virtual music using weights of music chosen by other users in context similar to active user`s context. Finally, we propose a method to recommend some pieces of music relative to user`s contexts and emotional states. Experimental results shows that the proposed method is more accurate than the traditional collaborative filtering method.
Music Recommendation;Emotional State;Contextual Information;Hybrid-Filtering;Winnow Algorithm;
 Cited by
The Relationship between the Social Interactions on the Social Network and the Purchase Intention,;

한국컴퓨터정보학회논문지, 2016. vol.21. 5, pp.149-160 crossref(new window)
Li-Hua Li, Rong-Wang Hsu, and Fu-Min Lee, "Review of Recommender Systems and Their Application," International Journal of Advanced Information Technologies, Vol. 6, No. 1, pp. 63-87, June 2012.

K. Satya Reddy, "Improving an aggregate recommendation diversity Using ranking-based tactics," International Journal of Computer Trends and Technology, Vol. 4, Issue 9, pp. 3178-3183, Sep. 2013.

Robert M. Bell, Yehuda Koren, and Chris Volinsky, "All Together Now: A Perspective on the Netflix Prize," Chance, Vol. 23, No. 1, pp. 24-29, April 2010. crossref(new window)

Robin Burke, "Hybrid Web Recommender Systems," The Adaptive Web, Vol. 4321, pp. 377-408, May 2007.

David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry, "Using collaborative filtering to weave an information tapestry," Communications of ACM, Vol. 35, No. 12, pp. 61-70, Dec. 1992.

Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175-186, Oct. 1994.

Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro, "Content-based Recommnder Systes: State of the Art and Trends," Recommender Systems Handbook, pp. 73-105, Jan. 2011.

Ivan Cantador, Alejandro Bellogin, and David Vallet, "Content-based Recommendation in Social Tagging Systems," In Proceedings of the 4th ACM Conference on Recommender Systems, pp. 237-240, Sep. 2010.

Jonathan T. Foote, "Content-Based Retrieval of Music and Audio," In Proceedings of SPIE Multimedia Storage Archiving Systems II, vol. 3229, pp. 138-147, Oct. 1997.

Bruce Krulwich, "Lifestyle Finder: Intelligent User Profiling Using Large-Scale Demographic Data," Artificial Intelligence Magazine, vol. 18, no. 2, pp. 37-45, July 1997.

Gediminas Adomavicius and Alexander Tuzhilin, "Context-Aware Recommender Systems," Recommender Systems Handbook, pp. 217-253, Oct. 2011.

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, "Item-based Collaborative Filtering Recommendation Algorithm," In Proceedings of the 10th International Conference on World Wide Web, pp. 285-295, May 2001.

Paul Lamere, "Social Tagging and Music Information Retrieval," Journal of New Music Research, Vol. 37, No. 2, pp. 101-114, June 2008. crossref(new window)

George Tzanetakis and Perry Cook, "Musical genre classification of audio signals," IEEE Transactions on Speech and Audio Processing, Vol. 10, No. 5, pp. 293-302, July 2002. crossref(new window)

Robin Burke, "Hybrid Recommender Systems: Survey and Experiments," User Modeling and User-Adapted Interaction, Vol. 12, No. 4, pp. 331-370, Nov. 2002. crossref(new window)

Kyoung-Jae Kim and Hyun-chul Ahn, "Hybrid Recommender Systems using Social Network Analysis," In Proceedings of World Academy of Science, Engineering and Technology, pp. 879-882, Oct. 2012

Faustino Sanchez, Marta Barrilero, Silvia Uribe, Federico Alvarez, Agustin Tena, and Jose Manuel Menendez, "Social and Content Hybrid Image Recommender System for Mobile Social Networks," Mobile Networks and Applications, Vol. 17, Issue 6, pp. 782-795, Dec. 2012. crossref(new window)

Anind K. Dey and Gregory D. Abowd, "Towards a Better Understanding of Context and Context-Awaremess," In Proceedings of the 1st International Symposium on Handheld and Ubiquitous Computing, pp. 304-307, June 1999.

Rosalind W. Picard, "Affective Computing," MIT Media Laboratory Perceptual Computing Section Technical Report, No. 321, Nov. 1995.

Rosalind W. Picard, Affective Computing, The MIT Press, Oct. 1997.

Pero Subasic and Alison Huettner, "Affect Analysis of Text Using Fuzzy Semantic Typing," IEEE Transactions on Fuzzy Systems, Vol. 9, Issue 4, Aug. 2001.

Changhua Yang, Kevin Hsin-Yih Lin, and Hsin-Hsi Chen, "Building Emotion Lexicon from Weblog Corpora," In Proceedings of the 45th Annual Meeting of the ACL, pp. 133-136, June 2007.

Andrea Esuli and Fabrizio Sebastiani, "SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining," In Proceedings of the 5th Conference on Language Resources and Evaluation, pp. 417-422, May 2006.

Nick Littlestone, "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm," Machine Learning, Vol. 2, Issue 4, pp. 285-318, April 1988.