A hybrid social-acoustic recommendation system for popular music

ABSTRACT

Recommendation systems leverage several types of information relating to a recommendable item. The recommendation methods are often based on the analysis of how a set of users associate or rate a given set of items, but they can also focus on the analysis of how the content of the items is related. This paper discusses a hybrid recommendation system for music - a system that leverages both spectral graph properties of an item-based collaborative filtering association network as well as acoustic features of the underlying music signal. Both features are balanced appropriately and used to disambiguate the music-seeking intentions of a user.

REFERENCES

• 1: Marko Balabanović , Yoav Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [doi>10.1145/245108.245124]
• 2: Adam Berenzweig , Beth Logan , Daniel P. W. Ellis , Brian P. W. Whitman, A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures, Computer Music Journal, v.28 n.2, p.63-76, June 2004 [doi>10.1162/014892604323112257]
• 3: James Bergstra , Norman Casagrande , Dumitru Erhan , Douglas Eck , Balázs Kégl, Aggregate features and ADABOOST for music classification, Machine Learning, v.65 n.2-3, p.473-484, December 2006 [doi>10.1007/s10994-006-9019-7]
• 4: E. Blanzieri and P. Giorgini. From collaborative filtering to implicit culture: a general agent-based framework. In Proceedings of the Workshop on Agents and Recommender Systems, 2000.
• 5: Hung-Chen Chen , Arbee L. P. Chen, A music recommendation system based on music data grouping and user interests, Proceedings of the tenth international conference on Information and knowledge management, October 05-10, 2001, Atlanta, Georgia, USA [doi>10.1145/502585.502625]
• 6: William W. Cohen , Wei Fan, Web-collaborative filtering: recommending music by crawling the Web, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.33 n.1-6, p.685-698, June 2000
• 7: Sally Jo Cunningham , Nina Reeves , Matthew Britland, An ethnographic study of music information seeking: implications for the design of a music digital library, Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, May 27-31, 2003, Houston, Texas
• 8: S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391--407, 1990.
• 9: Julie Falkner , Franz Rendl , Henry Wolkowicz, A computational study of graph partitioning, Mathematical Programming: Series A and B, v.66 n.2, p.211-239, Sept. 7, 1994 [doi>10.1007/BF01581147]
• 10: David Goldberg , David Nichols , Brian M. Oki , Douglas Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, v.35 n.12, p.61-70, Dec. 1992 [doi>10.1145/138859.138867]
• 11: J. Y. Kim and N. J. Belkin. Categories of music description and search terms and phrases used by non-music experts. In ISMIR 2002: Proceedings of the 3th International Conference on Music Information Retrieval, 2002.
• 12: D. Kirovski and H. Attias. Beat-id: identifying music via beat analysis. Multimedia Signal Processing, 2002 IEEE Workshop on, pages 190--193, 2002.
• 13: P. Knees, E. Pampalk, and G. Widmer. Artist classification with web-based data. In Proceedings of the 5th International Symposium on Music Information Retrieval (ISMIR '04), pages 517--524, 2004.
• 14: Qing Li , Byeong Man Kim , Dong Hai Guan , Duk whan Oh, A music recommender based on audio features, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009106]
• 15: Greg Linden , Brent Smith , Jeremy York, Amazon.com Recommendations: Item-to-Item Collaborative Filtering, IEEE Internet Computing, v.7 n.1, p.76-80, January 2003 [doi>10.1109/MIC.2003.1167344]
• 16: B. Logan. Music recommendation from song sets. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pages 425--428, 2004.
• 17: Prem Melville , Raymod J. Mooney , Ramadass Nagarajan, Content-boosted collaborative filtering for improved recommendations, Eighteenth national conference on Artificial intelligence, p.187-192, July 28-August 01, 2002, Edmonton, Alberta, Canada
• 18: F. Pachet and D. Cazaly. A taxonomy of musical genres. Proc. Content-Based Multimedia Information Access (RIAO), pages 1238--1245, 2000.
• 19: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]
• 20: U. Shardanand. Social information filtering for music recommendation. SM Thesis, Program in Media Arts and Sciences, Massachusetts Institute of Technology, 1994.
• 21: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]
• 22: G. Tzanetakis and P. Cook. Musical genre classification of audio signals. Speech and Audio Processing, IEEE Transactions on, 10(5):293--302, 2002.
• 23: S. Vembu and S. Baumann. A self-organizing map based knowledge discovery for music recommendation systems. Proc. of the 2nd Internation Symposium on Computer Music Modeling and Retrieval (CMMR '04), Esbjerg, Denmark, 2004.
• 24: Kris West , Paul Lamere, A model-based approach to constructing music similarity functions, EURASIP Journal on Applied Signal Processing, v.2007 n.1, p.149-149, 1 January 2007 [doi>10.1155/2007/24602]
• 25: Erling Wold , Thom Blum , Douglas Keislar , James Wheaton, Content-Based Classification, Search, and Retrieval of Audio, IEEE MultiMedia, v.3 n.3, p.27-36, September 1996 [doi>10.1109/93.556537]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models; Information filtering

General Terms:
Algorithms, Theory

Keywords:
acoustic, hybrid, music, recommendation, social