Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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A Study on Serendipity-Oriented Music Recommendation Based on Play Information

재생 정보 기반 우연성 지향적 음악 추천에 관한 연구

  • Ha, Taehyun (Department of Interaction Science, Sungkyunkwan University) ;
  • Lee, Sangwon (Department of Interaction Science, Sungkyunkwan University)
  • 하태현 (성균관대학교 인터랙션사이언스학과) ;
  • 이상원 (성균관대학교 인터랙션사이언스학과)
  • Received : 2014.09.22
  • Accepted : 2014.12.01
  • Published : 2015.04.15
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Abstract

With the recent interests with culture technologies, many studies for recommendation systems have been done. In this vein, various music recommendation systems have been developed. However, they have often focused on the technical aspects such as feature extraction and similarity comparison, and have not sufficiently addressed them in user-centered perspectives. For users' high satisfaction with recommended music items, it is necessary to study how the items are connected to the users' actual desires. For this, our study proposes a novel music recommendation method based on serendipity, which means the freshness users feel for their familiar items. The serendipity is measured through the comparison of users' past and recent listening tendencies. We utilize neural networks to apply these tendencies to the recommendation process and to extract the features of music items as MFCCs (Mel-frequency cepstral coefficients). In that the recommendation method is developed based on the characteristics of user behaviors, it is expected that user satisfaction for the recommended items can be actually increased.

Keywords

References

  1. Adomavicius, G. and Tuzhilin, A. (2005), Toward the next generation of recommender systems : A survey of the state-of-the-art and possible extensions, Knowledge and Data Engineering, IEEE Transactions on, 17(6), 734-749. https://doi.org/10.1109/TKDE.2005.99
  2. Ansari, A., Essegaier, S., and Kohli, R. (2000), Internet recommendation systems, Journal of Marketing research, 37(3), 363-375. https://doi.org/10.1509/jmkr.37.3.363.18779
  3. Barrichelo, V. M. O., Heuer, R. J., Dean, C. M., and Sataloff, R. T. (2001), Comparison of singer's formant, speaker's ring, and LTA spectrum among classical singers and untrained normal speakers, Journal of voice, 15(3), 344-350. https://doi.org/10.1016/S0892-1997(01)00036-4
  4. Beel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., and Nurnberger, A. (2013), Research paper recommender system evaluation : a quantitative literature survey, In Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation, 15-22.
  5. Bobadilla, J., Ortega, F., Hernando, A., and Gutierrez, A. (2013), Recommender systems survey, Knowledge-Based Systems, 46, 109-132. https://doi.org/10.1016/j.knosys.2013.03.012
  6. Breese, J. S., Heckerman, D., and Kadie, C. (1998), Empirical analysis of predictive algorithms for collaborative filtering, In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 43-52.
  7. Bu, J., Tan, S., Chen, C., Wang, C., Wu, H., Zhang, L., and He, X. (2010), Music recommendation by unified hypergraph : combining social media information and music content, In Proceedings of the international conference on Multimedia, 391-400.
  8. Celma, O. (2010), Music recommendation and discovery : The long tail, long fail, and long play in the digital music space : Springer.
  9. Celma, O. and Serra, X. (2008), FOAFing the music : Bridging the semantic gap in music recommendation, Web Semantics : Science, Services and Agents on the World Wide Web, 6(4), 250-256. https://doi.org/10.1016/j.websem.2008.09.004
  10. Chee, S. H. S., Han, J., and Wang, K. (2001), Rectree : An efficient collaborative filtering method, In Data Warehousing and Knowledge Discovery, 141-151.
  11. Chen, H. C. and Chen, A. L. (2005), A music recommendation system based on music and user grouping, Journal of Intelligent Information Systems, 24(2/3), 113-132. https://doi.org/10.1007/s10844-005-0319-3
  12. Cheng, B. and Titterington, D. M. (1994), Neural networks : A review from a statistical perspective, Statistical science, 2-30.
  13. Good, N., Schafer, J. B., Konstan, J. A., Borchers, A. I., Sarwar, B., Herlocker, J., and Riedl, J. (1999), Combining collaborative filtering with personal agents for better recommendations, In AAAI/IAAI, 439-446.
  14. Guyon, I. (1991), Applications of neural networks to character recognition, International Journal of Pattern Recognition and Artificial Intelligence, 5(01n02), 353-382. https://doi.org/10.1142/S021800149100020X
  15. Han, J., Kamber, M., and Pei, J. (2006), Data mining : concepts and techniques : Morgan kaufmann.
  16. Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. (2004), Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), 22(1), 5-53. https://doi.org/10.1145/963770.963772
  17. Hotta, H. and Hagiwara, M. (2007), User profiling system using social networks for recommendation, In Proceedings of International Symposium on Advanced Intelligent Systems, 207-274.
  18. Hull, G. P., Hutchison, T. W., and Strasser, R. (2011), The Music Business and Recording Industry : Delivering Music in the 21st Century : Taylor and Francis.
  19. Iaquinta, L., De Gemmis, M., Lops, P., Semeraro, G., Filannino, M., and Molino, P. (2008), Introducing serendipity in a content-based recommender system, In Hybrid Intelligent Systems, HIS Eighth International Conference on, 168-173.
  20. Im, Y., Jung, E. S., and Park, S. (2008), Development of an ergonomic product development process reflecting quantified customer preference, Journal of the Korean Institute of Industrial Engineers, 34(1), 66-78.
  21. Kang, Y. and Bae, J. (2013), Customized web search rank provision, Journal of the Korean Institute of Industrial Engineers, 39(2), 119-128. https://doi.org/10.7232/JKIIE.2013.39.2.119
  22. Kim, B. M., Li, Q., Kim, J. W., and Kim, J. (2004), A new collaborative recommender system addressing three problems, In PRICAI : Trends in Artificial Intelligence, 495-504.
  23. Kordumova, S., Kostadinovska, I., Barbieri, M., Pronk, V., and Korst, J. (2010), Personalized implicit learning in a music recommender system, In 18th International Conference, UMAP, 351-362.
  24. Leong, T. W., Vetere, F., and Howard, S. (2012), Experiencing coincidence during digital music listening, ACM Transactions on Computer-Human Interaction (TOCHI), 19(1), 6.
  25. Li, Q., Myaeng, S. H., Guan, D. H., and Kim, B. M. (2005), A probabilistic model for music recommendation considering audio features, In Information retrieval technology, 72-83.
  26. Liu, N. H., Hsieh, S. J., and Tsai, C. F. (2010), An intelligent music playlist generator based on the time parameter with artificial neural networks, Expert Systems with Applications, 37(4), 2815-2825. https://doi.org/10.1016/j.eswa.2009.09.009
  27. Lops, P., de Gemmis, M., and Semeraro, G. (2011), Content-based recommender systems: State of the art and trends, Recommender Systems Handbook, 73-105.
  28. Magno, T. and Sable, C. (2008), A Comparison of Signal Based Music Recommendation to Genre Labels, Collaborative Filtering, Musicological Analysis, Human Recommendation and Random Baseline, In ISMIR, 161-166.
  29. McNee, S. M., Riedl, J., and Konstan, J. A. (2006), Being accurate is not enough : how accuracy metrics have hurt recommender systems, In CHI extended abstracts on Human factors in computing systems, 1097-1101.
  30. Passant, A. and Raimond, Y. (2008), Combining Social Music and Semantic Web for music-related recommender systems, In The 7th International Semantic Web Conference, 19.
  31. Pu, P., Chen, L., and Hu, R. (2012), Evaluating recommender systems from the user's perspective : survey of the state of the art, User Modeling and User-Adapted Interaction, 22(4-5), 317-355. https://doi.org/10.1007/s11257-011-9115-7
  32. Russell, S. J. and Norvig, P. (2009), Artificial intelligence : a modern approach, Prentice Hall.
  33. Sarwar, B., Karypis, G., Konstan, J. A., and Riedl, J. (2000), Application of dimensionality reduction in recommender system-a case study : DTIC Document.
  34. Sarwar, B., Karypis, G., Konstan, J. A., and Riedl, J. (2001), Item-based collaborative filtering recommendation algorithms, In Proceedings of the 10th international conference on World Wide Web, 285-295.
  35. Song, Y., Dixon, S., and Pearce, M. (2012), A survey of music recommendation systems and future perspectives, In 9th international symposium on computer music modelling and retrieval (CMMR 2012), 19-22.
  36. Stumpf, S. and Muscroft, S. (2011), When users generate music playlists : When words leave off, music begins? In Multimedia and Expo (ICME), 2011 IEEE International Conference on, 1-6.
  37. Swearingen, K. and Sinha, R. (2001), Beyond algorithms : An HCI perspective on recommender systems, In ACM SIGIR 2001 Workshop on Recommender Systems, 393-408.
  38. Tintarev, N. and Masthoff, J. (2011), Designing and evaluating explanations for recommender systems, Recommender Systems Handbook, 479-510.
  39. Uitdenbogerd, A. L. and van Schyndel, R. G. (2002), A Review of Factors Affecting Music Recommender Success, In ISMIR, 2, 204-208.
  40. Ward, M. K., Goodman, J. K., and Irwin, J. R. (2013), The same old song : The power of familiarity in music choice, Marketing Letters, 1-11.
  41. Wilson, T. D. (2009), Know thyself, Perspectives on Psychological Science, 4(4), 384-389. https://doi.org/10.1111/j.1745-6924.2009.01143.x
  42. 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 ISMIR, 6, 7th.
  43. Zhang, Y. C., Seaghdha, D. O., Quercia, D., and Jambor, T. (2012), Auralist : introducing serendipity into music recommendation, In Proceedings of the fifth ACM international conference on Web search and data mining, 13-22.