Markus Schedl
David Hauger
ABSTRACT
A shortcoming of current approaches for music recommendation is that they consider user-specific characteristics only on a very simple level, typically as some kind of interaction between users and items when employing collaborative filtering. To alleviate this issue, we propose several user features that model aspects of the user's music listening behavior: diversity, mainstreaminess, and novelty of the user's music taste. To validate the proposed features, we conduct a comprehensive evaluation of a variety of music recommendation approaches (stand-alone and hybrids) on a collection of almost 200 million listening events gathered from \propername{Last.fm}. We report first results and highlight cases where our diversity, mainstreaminess, and novelty features can be beneficially integrated into music recommender systems.
- G. Adomavicius and A. Tuzhilin. Recommender Systems Handbook, chapter Context-Aware Recommender Systems, pages 217--253. Springer, 2011.Google Scholar
Cross Ref
- L. Baltrunas, M. Kaminskas, B. Ludwig, O. Moling, F. Ricci, K.-H. Lüke, and R. Schwaiger. InCarMusic: Context-Aware Music Recommendations in a Car. In Proc. EC-Web, 2011.Google ScholarCross Ref
- O. Celma. Music Recommendation and Discovery -- The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, 2010. Google ScholarDigital Library
- Z. Cheng and J. Shen. Just-for-Me: An Adaptive Personalization System for Location-Aware Social Music Recommendation. In Proc. ICMR, Glasgow, UK, April 2014. Google ScholarDigital Library
- J.-C. de Borda. Mémoire sur les élections au scrutin. Histoire de l'Académie Royale des Sciences, 1781.Google Scholar
- F. Debole and F. Sebastiani. Supervised Term Weighting for Automated Text Categorization. In Proc. SAC, 2003. Google ScholarDigital Library
- M. Kaminskas, F. Ricci, and M. Schedl. Location-aware Music Recommendation Using Auto-Tagging and Hybrid Matching. In Proc. RecSys, Hong Kong, China, 2013. Google ScholarDigital Library
- H.-S. Park, J.-O. Yoo, and S.-B. Cho. A context-aware music recommendation system using fuzzy bayesian networks with utility theory. In FSKD. LNCS (LNAI), pages 970--979. Springer, 2006. Google ScholarDigital Library
- B. Prajapati, M. Dunne, and R. Armstrong. Sample Size Estimation and Statistical Power Analyses. Optometry Today, 16(7), 2010.Google Scholar
- M. Schedl, E. Gómez, and J. Urbano. Music information retrieval: Recent developments and applications. Foundations and Trends in Information Retrieval, 8(2--3):127--261, 2014. Google ScholarDigital Library
- M. Schedl, D. Hauger, K. Farrahi, and M. Tkalcic. On the Influence of User Characteristics on Music Recommendation. In Proc. ECIR, Vienna, Austria, 2015.Google ScholarCross Ref
- Y. Shi, M. Larson, and A. Hanjalic. Collaborative Filtering Beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges. ACM Comput. Surv., 47(1):3:1--3:45, May 2014. Google ScholarDigital Library
- X. Wang, D. Rosenblum, and Y. Wang. Context-aware mobile music recommendation for daily activities. In Proc. ACM Multimedia, 2012. Google ScholarDigital Library
- Yuan Cao Zhang, Diarmuid O Seaghdha, Daniele Quercia, Tamas Jambor. Auralist: Introducing Serendipity into Music Recommendation. In Proc. WSDM, 2012. Google ScholarDigital Library
Index Terms
- Tailoring Music Recommendations to Users by Considering Diversity, Mainstreaminess, and Novelty
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