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Proposal of Context-Aware Music Recommender System Using Negative Sampling

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Book cover Advances in Artificial Intelligence (JSAI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1128))

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Abstract

This is an extension from a selected paper from JSAI2019. This paper proposes a method for recommending music items considering listeners’ context information. Recently, users can enjoy music easily regardless of time and a place due to evolution of online music services such as Spotify. However, it is difficult for us to find appropriate music items from enormous resources. On the other hand, because of listening style and characteristic of music items, music items do not usually have explicit rating. Therefore, implicit feedback such as playing count has been widely used to construct recommender systems. As additional information, this paper considers listeners’ context. The proposed method employs FMs (Factorization Machines), in which the context information is treated as factors. Negative sampling is applied to reduce the number of negative samples (music items a user has yet to be listened). The effectiveness of the proposed method and the effect of negative sampling are evaluated with an offline experiment. The experimental result on nowplaying-rs dataset shows that the proposed method outperforms wALS (weighted Alternating Least Squares) method. Furthermore, different negative sampling methods such as popularity-based one and sampling with different time window size are also investigated.

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Notes

  1. 1.

    https://www.macobserver.com/news/songs-in-streaming-music-service-libraries/.

  2. 2.

    http://dbis-nowplaying.uibk.ac.at/#nowplayingrs.

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Authors and Affiliations

  1. Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo, 191-0065, Japan

    Jin-cheng Zhang & Yasufumi Takama

Authors
  1. Jin-cheng Zhang
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  2. Yasufumi Takama
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Corresponding author

Correspondence to Jin-cheng Zhang .

Editor information

Editors and Affiliations

  1. Department of Systems Innovation, University of Tokyo, Tokyo, Japan

    Yukio Ohsawa

  2. Faculty of Business and Commerce, Kansai University, Osaka, Japan

    Katsutoshi Yada

  3. Nagoya Institute of Technology, Nagoya, Japan

    Takayuki Ito

  4. Graduate School of System Design, Tokyo Metropolitan University, Tokyo, Japan

    Yasufumi Takama

  5. Department of Information and Communication, Tokyo Metropolitan University, Tokyo, Japan

    Eri Sato-Shimokawara

  6. Faculty of Letters, Chiba University, Chiba, Japan

    Akinori Abe

  7. School of Engineering, The University of Tokyo, Tokyo, Japan

    Junichiro Mori

  8. Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan

    Naohiro Matsumura

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Zhang, Jc., Takama, Y. (2020). Proposal of Context-Aware Music Recommender System Using Negative Sampling. In: Ohsawa, Y., et al. Advances in Artificial Intelligence. JSAI 2019. Advances in Intelligent Systems and Computing, vol 1128. Springer, Cham. https://doi.org/10.1007/978-3-030-39878-1_11

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