Improving Recommendation Diversity Across Users by Reducing Frequently Recommended Items

Seiki Miyamoto, Takumi Zamami, Hayato Yamana

研究成果: Conference contribution

4 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Naoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
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 - 2018 7月 2
イベント2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
継続期間: 2018 12月 102018 12月 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
国/地域United States
CitySeattle
Period18/12/1018/12/13

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 情報システム

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