Point of Interest Recommendation Acceleration Using Clustering

Huida Jiao, Fan Mo, Hayato Yamana

研究成果: Conference contribution

抄録

Point of Interest (POI) recommendation systems exploit information in location-based social networks to predict locations that users may be interested in. POI recommendations have been widely adopted in many applications, which are helpful for daily life. POI recommendation services receive a huge volume of visit history data generated by users' daily lives with mobile devices. However, POI recommendation systems require long time to build a model from such a huge volume of check-in data and recommend suitable POIs to users. Thus, it is indispensable to shorten the execution time in a big data era. In this study, we propose a clustering-based method to divide the data into multiple subsets to accelerate the POI recommendation's execution while maintaining accuracy. Our proposed method can be adapted to any general POI recommendation algorithm. We divide the whole data, that is, users and POIs, into subsets with a tree structure to balance the size of subsets according to both geographical information and user check-in distribution. Evaluation results show that we successfully accelerate the base algorithms over 17 to 39 times faster while keeping the accuracy almost the same.

本文言語English
ホスト出版物のタイトル2021 IEEE 6th International Conference on Big Data Analytics, ICBDA 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ175-180
ページ数6
ISBN(電子版)9780738131672
DOI
出版ステータスPublished - 2021 3 5
イベント6th IEEE International Conference on Big Data Analytics, ICBDA 2021 - Xiamen, China
継続期間: 2021 3 52021 3 8

出版物シリーズ

名前2021 IEEE 6th International Conference on Big Data Analytics, ICBDA 2021

Conference

Conference6th IEEE International Conference on Big Data Analytics, ICBDA 2021
国/地域China
CityXiamen
Period21/3/521/3/8

ASJC Scopus subject areas

  • 情報システム
  • 情報システムおよび情報管理
  • 人工知能
  • コンピュータ サイエンスの応用

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