Kibeom Lee
ABSTRACT
Collaborative filtering, being a popular method for generating recommendations, produces satisfying results for users by providing extremely relevant items. Despite being popular, however, this method is prone to many problems. One of these problems is popularity bias, in which the system becomes skewed towards items that are popular amongst the general user population. These 'obvious' items are, technically, extremely relevant items but fail to be novel. In this paper, we maintain using collaborative filtering methods while still managing to produce novel yet relevant items. This is achieved by utilizing the long-tailed distribution of listening behavior of users, in which their playlists are biased towards a few songs while the rest of the songs, those in the long tail, have relatively low play counts. In addition, we also apply a link analysis method to users and define links between them to create an increasingly fine-grained approach in calculating weights for the recommended items. The proposed recommendation method was available online as a user study in order to measure the relevancy and novelty of the recommended items. Results show that the algorithm manages to include novel recommendations that are still relevant, and shows the potential for a new way of generating novel recommendations.
- C. Anderson. The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion, 2006. Google Scholar
Digital Library
- E. Brynjolfsson, Y. J. Hu, and D. Simester. Goodbye pareto principle, hello long tail: The effect of search costs on the concentration of product sales. Technical report, Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER), Purdue University - Krannert School of Management, MIT Sloan School of Management, 2007.Google Scholar
- Ò. Celma. Foafing the music: Bridging the semantic gap in music recommendation. In Proceedings of the 5th International Semantic Web Conference, pages 927--934. Springer, 2006. Google ScholarDigital Library
- Ò. Celma and P. Lamere. If you like the beatles you might like...: a tutorial on music recommendation. In A. El-Saddik, S. Vuong, C. Griwodz, A. D. Bimbo, K. S. Candan, and A. Jaimes, editors, ACM Multimedia, pages 1157--1158. ACM, 2008. Google ScholarDigital Library
- Y.-C. Chen, R.-A. Shang, and C.-Y. Kao. The effects of information overload on consumers' subjective state towards buying decision in the internet shopping environment. Electron. Commer. Rec. Appl., 8:48--58, January 2009. Google ScholarDigital Library
- D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM, 35(12):61--70, 1992. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, and J. T. Riedl. Evaluating collaborative filtering recommendations. In Computer Supported Cooperative Work, pages 241--250, 2000. Google ScholarDigital Library
- W. Hill, L. Stead, M. Rosenstein, and G. Furnas. Recommending and evaluating choices in a virtual community of use. In CHI '95: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 194--201, New York, NY, USA, 1995. ACM Press/Addison-Wesley Publishing Co. Google ScholarDigital Library
- Z. Huang, D. Zeng, and H. Chen. A link analysis approach to recommendation under sparse data. In Proceedings of the Tenth Americas Conference on Information Systems, New York, NY, USA, 2004.Google Scholar
- J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM, 40(3):77--87, 1997. Google ScholarDigital Library
- K. Lee, W. S. Yeo, and K. Lee. Music recommendation in the personal long tail: Using a social-based analysis of a user's long-tailed listening behavior. In Proceedings of the Workshop on Music Recommendation and Discovery, pages 47--54, 2010.Google Scholar
- M. Levy and M. Sandler. A semantic space for music derived from social tags. In Proceedings of the 8th International Conference on Music Information Retrieval, Vienna, Austria, 2007.Google Scholar
- G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76--80, January 2003. Google ScholarDigital Library
- L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab, November 1999. Previous number = SIDL-WP-1999-0120.Google Scholar
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pages 175--186. ACM Press, 1994. Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proceedings of the Second ACM Conference on Electronic Commerce (EC'00), pages 285--295, 2000. Google ScholarDigital Library
- J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative filtering recommender systems, 2007.Google Scholar
- U. Shardanand and P. Maes. Social information filtering: algorithms for automating "word of mouth". In CHI '95: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 210--217, New York, NY, USA, 1995. ACM Press/Addison-Wesley Publishing Co. Google ScholarDigital Library
Index Terms
- My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation
Recommendations
Optimizing multiple objectives in collaborative filtering
RecSys '10: Proceedings of the fourth ACM conference on Recommender systemsThis paper is about the utility of making personalized recommendations. While it is important to accurately predict the target user's preference, in practice the accuracy should not be the only concern; a useful recommender system needs to consider the ...
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
ICDM '10: Proceedings of the 2010 IEEE International Conference on Data MiningCold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, ...
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Comments