Point of interest recommendation by exploiting geographical weighted center and categorical preference

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Point of interest (POI) recommendation is one of the indispensable services in location-based social networks (LBSNs). POI recommendation helps users find new locations and better understand the city. In LBSNs, the aspects, such as geographical information and categorical information, improve the accuracy of POI recommendation. In this paper, we propose two new techniques to improve the recommendation accuracy; 1) weighted center of a target user's each active area and 2) category-dependent threshold for categorical preference. The weighted center represents density-based center of a target user's active area. The geographical aspect usually adopts the target user's active areas that he frequently visited. Although previous researches define the active area by its active center and its radius, they choose the location of the most frequently visited POI as the active center even if there exist several POIs that have similar number of check-ins, which results in miss-definition of active center. Our weighted center is able to handle the target user's check-in probability, which follows a power-law distribution. Besides, previous researches predict users' preference for categories; however, they neglect the fact that different categories have different users' preference distributions. For example, a specific category has wide-range of subcategories to be preferred by user, but another category has a few subcategories to be preferred, even if there are many subcategories in the category. Thus, we set different thresholds to select candidate subcategories in each category. Experimental result with Weeplaces dataset shows that our method outperforms other baselines by at least 16.93% in F1-score@5.

本文言語English
ホスト出版物のタイトルProceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
編集者Panagiotis Papapetrou, Xueqi Cheng, Qing He
出版社IEEE Computer Society
ページ73-76
ページ数4
ISBN(電子版)9781728146034
DOI
出版ステータスPublished - 2019 11
イベント19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China
継続期間: 2019 11 82019 11 11

出版物シリーズ

名前IEEE International Conference on Data Mining Workshops, ICDMW
2019-November
ISSN(印刷版)2375-9232
ISSN(電子版)2375-9259

Conference

Conference19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
国/地域China
CityBeijing
Period19/11/819/11/11

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • ソフトウェア

引用スタイル