Shulong Tan
Skip Abstract Section
Abstract
There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. We propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations.
- Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 6, 734--749. Google Scholar
Digital Library
- Agarwal, S. 2006. Ranking on graph data. In Proceedings of the 23rd International Conference on Machine Learning. Google ScholarDigital Library
- Agarwal, S., Branson, K., and Belongie, S. 2006. Higher order learning with graphs. In Proceedings of the 23rd International Conference on Machine Learning. Google ScholarDigital Library
- Aucouturier, J.-J. and Pachet, F. 2002. Scaling up music playlist generation. In Proceedings of the IEEE International Conference on Multimedia and Expo.Google Scholar
- Berenzweig, A., Logan, B., W.Ellis, D. P., and Whitman, B. 2004. A large-scale evaluation of acoustic and subjective music-similarity measures. Comput. Music J. 28, 2, 63--76. Google ScholarDigital Library
- Breese, J., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Google ScholarDigital Library
- Bu, J., Tan, S., Chen, C., Wang, C., Wu, H., and He, X. 2010. Music recommendation by unified hypergraph: Combining social media information and music content. In Proceedings of the 18th ACM International Conference on Multimedia. Google ScholarDigital Library
- Bulò, S. R. and Pelillo, M. 2009. A game-theoretic approach to hypergraph clustering. Adv. Neural Inf. Proc. Syst. 22.Google Scholar
- Cai, R., Zhang, C., Zhang, L., and Ma, W.-Y. 2007. Scalable music recommendation by search. In Proceedings of the 15th International Conference on Multimedia. Google ScholarDigital Library
- Cano, P., Koppenberger, M., and Wack, N. 2005. Content-based music audio recommendation. In Proceedings of the 13th ACM International Conference on Multimedia. Google ScholarDigital Library
- Celma, Ò. 2006. Foafing the music: Bridging the semantic gap in music recommendation. In Proceedings of the 5th International Semantic Web Conference. Google ScholarDigital Library
- Celma, Ò. and Lamere, P. 2008. If you like the Beatles you might like&hellips;: A tutorial on music recommendation. In Proceedings of the 16th ACM International Conference on Multimedia. Google ScholarDigital Library
- Chen, S., Wang, F., and Zhang, C. 2007. Simultaneous heterogeneous data clustering based on higher order relationships. In Proceedings of the 7th IEEE International Conference on Data Mining Workshops. Google ScholarDigital Library
- Diederich, J. and Iofciu, T. 2006. Finding communities of practice from user profiles based on folksonomies. In Proceedings of the 1st International Workshop on Building Technology Enhanced Learning Solutions for Communities of Practice.Google Scholar
- Donaldson, J. 2007. A hybrid social-acoustic recommendation system for popular music. In Proceedings of the ACM Conference on Recommender Systems. Google ScholarDigital Library
- Frieze, A., Kannan, R., and Vempala, S. 2004. Fast Monte-Carlo algorithms for finding low-rank approximations. J. ACM 51, 6, 1025--1041. Google ScholarDigital Library
- Guan, Z., Bu, J., Mei, Q., Chen, C., and Wang, C. 2009. Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In Proceedings of the 32nd ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- Guan, Z., Wang, C., Bu, J., Chen, C., Yang, K., Cai, D., and He, X. 2010. Document recommendation in social tagging services. In Proceedings of the 19th International Conference on World Wide Web (WWW'10). Google ScholarDigital Library
- Harpale, A. S. and Yang, Y. 2008. Personalized active learning for collaborative filtering. In Proceedings of the 31st ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- Knees, P., Pohle, T., Schedl, M., and Widmer, G. 2006. Combining audio-based similarity with web-based data to accelerate automatic music playlist generation. In Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval. Google ScholarDigital Library
- Konstas, I., Stathopoulos, V., and Jose, J. M. 2009. On social networks and collaborative recommendation. In Proceedings of the 32nd ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- Li, Q., Myaeng, S. H., and Kim, B. M. 2007. A probabilistic music recommender considering user opinions and audio features. Infor. Process. Manag. 43, 2, 473--487. Google ScholarDigital Library
- Lin, Y., Sun, J., Castro, P., Konuru, R., Sundaram, H., and Kelliher, A. 2009. Metafac: community discovery via relational hypergraph factorization. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Google ScholarDigital Library
- Liu, N. N. and Yang, Q. 2008. Eigenrank: a ranking-oriented approach to collaborative filtering. In Proceedings of the 31st ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- Logan, B. 2004. Music recommendation from song sets. In Proceedings of the 5th International Conference on Music Information Retrieval.Google Scholar
- Logan, B. and Salomon, A. 2001. Music similarity function based on signal analysis. In Proceedings of IEEE International Conference on Multimedia and Expo.Google Scholar
- Lovász, L. 1993. Random walks on graphs: A survey. Combinatorics, Paul Erdos is Eighty 2, 1, 1--46.Google Scholar
- Ma, H., King, I., and Lyu, M. R. 2009. Learning to recommend with social trust ensemble. In Proceedings of the 32nd ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- McKay, C. and Fujinaga, I. 2008. Combining features extracted from audio, symbolic and cultural sources. In Proceedings of the 9th International Conference on Music Information Retrieval.Google Scholar
- Pauws, S., Verhaegh, W., and Vossen, M. 2006. Fast generation of optimal music playlists using local search. In Proceedings of the 7th International Conference on Music Information Retrieval.Google Scholar
- Ragno, R. J., Burges, C. J. C., and Herley, C. 2005. Inferring similarity between music objects with application to playlist generation. In Proceedings of the 7th ACM SIGMM Workshop on Multimedia Information Retrieval. Google ScholarDigital Library
- Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work. Google ScholarDigital Library
- Rho, S., jun Han, B., and Hwang, E. 2009. SVR-based music mood classification and context-based music recommendation. In Proceedings of the 17th ACM International Conference on Multimedia. Google ScholarDigital Library
- Rubner, Y., Tomasi, C., and Guibas, L. J. 2000. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vis. 40, 2, 99--121. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. Google ScholarDigital Library
- Sen, S., Vig, J., and Riedl, J. 2009. Tagommenders: Connecting users to items through tags. In Proceedings of the 18th International Conference on World Wide Web. Google ScholarDigital Library
- Song, Y., Zhuang, Z., Li, H., Zhao, Q., Li, J., Lee, W.-C., and Giles, C. L. 2008. Real-time automatic tag recommendation. In Proceedings of the 31st ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- Sun, L., Ji, S., and Ye, J. 2008. Hypergraph spectral learning for multi-label classification. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Google ScholarDigital Library
- Svizhenko, A., Anantram, M. P., Govindan, T. R., Biegel, B., and Venugopal, R. 2009. Two-dimensional quantum mechanical modeling of nanotransistors. J. Appl. Phys. 91, 4, 2343--2354.Google ScholarCross Ref
- Symeonidis, P., Ruxanda, M., Nanopoulos, A., and Manolopoulos, Y. 2008. Ternary semantic analysis of social tags for personalized music recommendation. In Proceedings of the 9th International Conference on Music Information Retrieval.Google Scholar
- Tao, D., Liu, H., and Tang, X. 2004. K-box: a query-by-singing based music retrieval system. In Proceedings of the 12th ACM International Conference on Multimedia. Google ScholarDigital Library
- Tiemann, M. and Pauws, S. 2007. Towards ensemble learning for hybrid music recommendation. In Proceedings of ACM Conference on Recommender Systems. Google ScholarDigital Library
- Tso-Sutterr, K. H. L., Marinho, L. B., and Schmidt-Thieme, L. 2008. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the ACM Symposium on Applied Computing. Google ScholarDigital Library
- Williams, C. and Williams, C. 2001. Using the nyström method to speed up kernel machines. In Adv. Neural Inf. Proc. Syst. 13.Google Scholar
- Yoshii, K. and Goto, M. 2009. Continuous pLSI and smoothing techniques for hybrid music recommendation. In Proceedings of the 10th International Society for Music Information Retrieval Conference.Google Scholar
- Yoshii, K., Goto, M., Komatani, K., Ogata, T., and Okuno, H. G. 2006. Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In Proceedings of the 7th International Conference on Music Information Retrieval.Google Scholar
- Zhang, Z., Zhou, T., and Zhang, Y. 2009. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Phys. A: Stat. Mech. Its Appl. 389, 1, 179--186.Google ScholarCross Ref
- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Schölkopf, B. 2003a. Learning with local and global consistency. In Adv. Neural Inf. Proc. Syst. 16.Google Scholar
- Zhou, D., Huang, J., and Schölkopf, B. 2006. Learning with hypergraphs: Clustering, classification, and embedding. In Adv. Neural Inf. Proc. Syst. 19.Google Scholar
- Zhou, D., Weston, J., Gretton, A., Bousquet, O., and Schölkopf, B. 2003b. Ranking on data manifolds. In Adv. Neural Inf. Proc. Syst. 16.Google Scholar
Index Terms
- Using rich social media information for music recommendation via hypergraph model
Recommendations
Music recommendation by unified hypergraph: combining social media information and music content
MM '10: Proceedings of the 18th ACM international conference on MultimediaAcoustic-based music recommender systems have received increasing interest in recent years. Due to the semantic gap between low level acoustic features and high level music concepts, many researchers have explored collaborative filtering techniques in ...
Measuring the Effect of Social Network Data on Music Recommendation
KMO '16: Proceedings of the The 11th International Knowledge Management in Organizations Conference on The changing face of Knowledge Management Impacting SocietyWith the rapid growth of online music market, online music providers are devoting on how to recommend suitable music for their customer to fit their interests. Music recommender system is therefore has been developed and researchers are focusing on how ...
Music Playlist Recommender System AFT-IS
ICCAE 2018: Proceedings of the 2018 10th International Conference on Computer and Automation EngineeringPreviously, we had proposed a playlist recommendation method to suggest music by considering the change in acoustic features in the song so that the transition between songs becomes smooth. Our previous method uses the last two songs in a playlist to ...
Comments