Abstract
Music playlist recommendation is an important component in modern music streaming services, which is used for improving user experience by regularly pushing personalized music playlists based on users’ preferences. In this paper, we propose a novel music playlist recommendation problem, namely Personalized Music Playlist Recommendation (PMPR), which aims to provide a suitable playlist for a user by taking into account her long/short-term preferences and music contextual data. We propose a data-driven framework, which is comprised of two phases: user/music feature extraction and music playlist recommendation. In the first phase, we adopt a matrix factorization technique to obtain long-term features of users and songs, and utilize the Paragraph Vector (PV) approach, an advanced natural language processing technique, to capture music context features, which are the basis of the subsequent music playlist recommendation. In the second phase, we design two Attention-based Long Short-Term Memory (AB-LSTM) models, i.e., typical AB-LSTM model and Improved AB-LSTM (IAB-LSTM) model, to achieve the suitable personalized playlist recommendation. Finally, we conduct extensive experiments using a real-world dataset, verifying the practicability of our proposed methods.
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Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR (2014)
Bogdanov, D., Haro, M., Fuhrmann, F., Xambó, A., Gómez, E., Herrera, P.: Semantic audio content-based music recommendation and visualization based on user preference examples. IPM 49(1), 13–33 (2013)
Cano, P., Koppenberger, M., Wack, N.: Content-based music audio recommendation. In: ACM Multimedia, pp. 211–212 (2005)
Cheng, Z., Shen, J.: Just-for-me: an adaptive personalization system for location-aware social music recommendation. In: ICMR, p. 185 (2014)
Cheng, Z., Shen, J.: On effective location-aware music recommendation. ACM TOIS 34(2), 13 (2016)
Knees, P., Schedl, M.: A survey of music similarity and recommendation from music context data. TOMCCAP 10(1), 2 (2013)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196 (2014)
Li, T., Ogihara, M., Li, Q.: A comparative study on content-based music genre classification. In: SIGIR, pp. 282–289 (2003)
McFee, B., Barrington, L., Lanckriet, G.: Learning content similarity for music recommendation. TASLP 20(8), 2207–2218 (2012)
Van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: NIPS, pp. 2643–2651 (2013)
Oramas, S., Ostuni, V.C., Noia, T.D., Serra, X., Sciascio, E.D.: Sound and music recommendation with knowledge graphs. ACM TIST 8(2), 21 (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: AUAI, pp. 452–461 (2009)
Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: WSDM, pp. 81–90 (2010)
Schedl, M., Pohle, T., Knees, P., Widmer, G.: Exploring the music similarity space on the web. ACM TOIS 29(3), 14 (2011)
Schmidhuber, J., Wierstra, D., Gomez, F.J.: Evolino: hybrid neuroevolution/optimal linear search for sequence prediction. In: IJCAI (2005)
Slaney, M.: Web-scale multimedia analysis: does content matter? IEEE Multimed. 18(2), 12–15 (2011)
Wang, D., Deng, S., Xu, G.: Sequence-based context-aware music recommendation. Inf. Retr. J. 21(2–3), 230–252 (2018)
Wang, X., Wang, Y.: Improving content-based and hybrid music recommendation using deep learning. In: ACM Multimedia, pp. 627–636 (2014)
Zangerle, E., Gassler, W., Specht, G.: Exploiting twitter’s collective knowledge for music recommendations. In: #MSM, pp. 14–17 (2012)
Acknowledgement
This work was supported by the NSFC (61832017, 61532018, 61836007, 61872235, 61729202, U1636210), and The National Key Research and Development Program of China (2018YFC1504504).
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Yang, H., Zhao, Y., Xia, J., Yao, B., Zhang, M., Zheng, K. (2019). Music Playlist Recommendation with Long Short-Term Memory. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_25
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DOI: https://doi.org/10.1007/978-3-030-18579-4_25
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