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Automatic playlist generation by applying tabu search

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Abstract

In this paper, we propose a solution to the problem of playlist generation. In order to capture user listening preference and recommend playlists, we maintain user profiles by keeping listening history. Then, we apply the sequential pattern mining algorithm with multiple minimum supports on user profiles to derive constraints. Given a set of derived constraints, we apply the tabu search to generate playlists which match constraints as much as possible. Finally, we implement our prototype and perform experiments to show the feasibility, efficiency, and effectiveness of our approach.

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Acknowledgments

The research was supported by Fu Jen Catholic University (Project No. 410031044042), and the National Science Council (NSC-100-2221-E-030-021 and NSC-101-2221-E-030-008).

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City, Taiwan, R.O.C

    Jia-Lien Hsu & Ya-Chao Lai

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  1. Jia-Lien Hsu
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  2. Ya-Chao Lai
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Correspondence to Jia-Lien Hsu.

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Hsu, JL., Lai, YC. Automatic playlist generation by applying tabu search. Int. J. Mach. Learn. & Cyber. 5, 553–568 (2014). https://doi.org/10.1007/s13042-013-0151-y

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