Abstract
Automatically generating playlists of music is an interesting area of research at present, with many online services now offering “radio channels” which attempt to play through sets of tracks a user is likely to enjoy. However, these tend to act as recommendation services, introducing a user to new music they might wish to listen to. Far less effort has gone into researching tools which learn an individual user’s tastes across their existing library of music and attempt to produce playlists fitting to their current mood. This paper describes a system that uses reinforcement learning over hierarchically-clustered sets of songs to learn a user’s listening preferences. Features extracted from the audio are also used as part of this process, allowing the software to create cohesive lists of tracks on demand or to simply play continuously from a given starting track. This new system is shown to perform well in a small user study, greatly reducing the relative number of songs that a user skips.
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References
Bellman, R.: A Markovian Decision Process. Journal of Mathematics and Mechanics 6 (1957)
Bertin-Mahieux, T., Ellis, D.P., Whitman, B., Lamere, P.: The Million Song Dataset. ISMIR 12 (2011)
Bu, J., Tan, S., Chen, C., Wang, C., Wu, H., Zhang, L., He, X.: Music Recommendation by Unified Hypergraph: Combining Social Media Information and Music Content. In: Proc. ICMR, pp. 391–400, New York, USA (2010)
Chen, S., Moore, J.L., Turnbull, D., Joachims, T.: Playlist Prediction via Metric Embedding. In: Proc. 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 714–722 (2012)
Cheng, Z., Shen, J.: Just-for-Me: An Adaptive Personalization System for Location-Aware Social Music Recommendation. In: Proc. ICMR (2014)
Chi, C.-Y., Lai, J.-Y., Tsai, R.T.-H., Jen Hsu, J.Y.: A Reinforcement Learning Approach to Emotion-based Automatic Playlist Generation. In: International Conference on Technologies and Applications of Artificial Intelligence, vol. 12, pp. 60–65 (2010)
Schafer, J., et al.: Collaborative filtering recommender systems. The Adaptive Web, 291–324 (2007)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative Filtering for Implicit Feedback Datasets. In: ICDM, pp. 263–272 (2008)
Jannach, D., Kamehkhosh, I., Bonnin, G.: Analyzing the Characteristics of Shared Playlists for Music Recommendation. In: Proceedings of the 6th Workshop on Recommender Systems and the Social Web (2014)
Koren, Y.: Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In: KDD (2008)
Liebman, E., Stone, P.: DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation. AAMAS 13 (2014)
McEnnis, D., Fujinaga, I., McKay, C., DePalle, P.: JAudio: A feature extraction library. In: ISMIR (2005)
McFee, B., Lanckriet, G.: The Natural Language of Playlists. In: Proc. ISMIR, Miami, FL, USA (October 2011)
McFee, B., Lanckriet, G.: Hypergraph Models of Playlist Dialects. In: Proc. ISMIR, Porto, Portugal (October 2012)
Pampalk, E., Pohle, T., Widmer, G.: Dynamic Playlist Generation Based on Skipping Behavior. In: Proc. ICMIR, pp. 634–637 (2005)
Pazzani, M.: Content-based recommendation systems. The Adaptive Web, 325–341 (2007)
Platt, J.C.: Fast embedding of sparse music similarity graphs. In: NIPS (2003)
Platt, J.C., Burges, C.J.C., Swenson, S., Weare, C., Zheng, A.: Learning a Gaussian Process Prior for Automatically Generating Music Playlists. Microsoft Corporation (2001)
Schedl, M., Breitschopf, G., Ionescu, B.: Mobile Music Genius: Reggae at the Beach, Metal on a Friday Night? In: Proc. ICMR (2014)
Watkins, C.: Learning from Delayed Rewards. PhD thesis, King’s College, University of Cambridge (1989)
Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model. Trans. Audio, Speech and Lang. Proc. 16(2), 435–447 (2008)
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King, J., Imbrasaitė, V. (2015). Generating Music Playlists with Hierarchical Clustering and Q-Learning. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_34
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DOI: https://doi.org/10.1007/978-3-319-16354-3_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
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