Abstract
In the music streaming service, users can feedback their preference explicitly or implicitly. We propose a link prediction approach for music recommendation with various types of user feedback to alleviate the data sparsity problem and the cold-start problem. Moreover, by reflecting the temporal features of the feedback, we analysis the time-varying user taste. The experiment on real-world dataset demonstrates the effectiveness of our approach in the recommendation quality.
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This work was partly supported by KAIST(A0601003029).
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Kim, N., Chae, WY., Lee, YJ. (2018). Music Recommendation with Temporal Dynamics in Multiple Types of User Feedback. In: Lee, W., Choi, W., Jung, S., Song, M. (eds) Proceedings of the 7th International Conference on Emerging Databases. Lecture Notes in Electrical Engineering, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-10-6520-0_35
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DOI: https://doi.org/10.1007/978-981-10-6520-0_35
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