Geographic diversification of recommended POIs in frequently visited areas

H. A.N. Jungkyu, Hayato Yamana

研究成果: Article査読

5 被引用数 (Scopus)

抄録

In the personalized Point-Of-Interest (POI) (or venue) recommendation, the diversity of recommended POIs is an important aspect. Diversity is especially important when POIs are recommended in the target users' frequently visited areas, because users are likely to revisit such areas. In addition to the (POI) category diversity that is a popular diversification objective in recommendation domains, diversification of recommended POI locations is an interesting subject itself. Despite its importance, existing POI recommender studies generally focus on and evaluate prediction accuracy. In this article, geographical diversification (geo-diversification), a novel diversification concept that aims to increase recommendation coverage for a target users' geographic areas of interest, is introduced, from which a method that improves geo-diversity as an addition to existing state-of-the-art POI recommenders is proposed. In experiments with the datasets from two real Location Based Social Networks (LSBNs), we first analyze the performance of four state-of-the-art POI recommenders from various evaluation perspectives including category diversity and geo-diversity that have not been examined previously. The proposed method consistently improves geo-diversity (CPR(geo)@20) by 5 to 12% when combined with four state-of-the-art POI recommenders with negligible prediction accuracy (Recall@20) loss and provides 6 to 18% geo-diversity improvement with tolerable prediction accuracy loss (up to 2.4%).

本文言語English
論文番号1
ジャーナルACM Transactions on Information Systems
38
1
DOI
出版ステータスPublished - 2019 10

ASJC Scopus subject areas

  • 情報システム
  • ビジネス、管理および会計(全般)
  • コンピュータ サイエンスの応用

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