Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items

Seiki Miyamoto, Takumi Zamami, Hayato Yamana

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    Recommender systems have been used for analyzing users' preference through their past activities and recommend items in which they might be interested in. There are numerous research on improving the accuracy of recommendation being conducted, so the recommender system reads user preference more accurately. However, it is important to consider the recommendation diversity, because lacking diversity will lead to recommendation being repetitive and obvious. In this paper, we propose a method to re-rank the recommendation list by appearance frequency of items to recommend more range of items. The experimental result shows that our method consistently performs better than a related work to improve recommendation diversity.

    本文言語English
    ホスト出版物のタイトルProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
    編集者Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ5392-5394
    ページ数3
    ISBN(電子版)9781538650356
    DOI
    出版ステータスPublished - 2019 1 22
    イベント2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
    継続期間: 2018 12 102018 12 13

    出版物シリーズ

    名前Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

    Conference

    Conference2018 IEEE International Conference on Big Data, Big Data 2018
    CountryUnited States
    CitySeattle
    Period18/12/1018/12/13

    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems

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