Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items

Seiki Miyamoto, Takumi Zamami, Hayato Yamana

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
    EditorsYang 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
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages5392-5394
    Number of pages3
    ISBN (Electronic)9781538650356
    DOIs
    Publication statusPublished - 2019 Jan 22
    Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
    Duration: 2018 Dec 102018 Dec 13

    Publication series

    NameProceedings - 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

    Fingerprint

    Recommender systems

    Keywords

    • collaborative filtering
    • diversity
    • recommender system

    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems

    Cite this

    Miyamoto, S., Zamami, T., & Yamana, H. (2019). Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, ... X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 5392-5394). [8622314] (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622314

    Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. / Miyamoto, Seiki; Zamami, Takumi; Yamana, Hayato.

    Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / 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. Institute of Electrical and Electronics Engineers Inc., 2019. p. 5392-5394 8622314 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Miyamoto, S, Zamami, T & Yamana, H 2019, Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. in Y Song, B Liu, K Lee, N Abe, C Pu, M Qiao, N Ahmed, D Kossmann, J Saltz, J Tang, J He, H Liu & X Hu (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622314, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 5392-5394, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 18/12/10. https://doi.org/10.1109/BigData.2018.8622314
    Miyamoto S, Zamami T, Yamana H. Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. In Song Y, Liu B, Lee K, Abe N, Pu C, Qiao M, Ahmed N, Kossmann D, Saltz J, Tang J, He J, Liu H, Hu X, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 5392-5394. 8622314. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). https://doi.org/10.1109/BigData.2018.8622314
    Miyamoto, Seiki ; Zamami, Takumi ; Yamana, Hayato. / Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / 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. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 5392-5394 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).
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    abstract = "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.",
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