Point of Interest Recommendation Acceleration Using Clustering

Huida Jiao, Fan Mo, Hayato Yamana

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

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publication2021 IEEE 6th International Conference on Big Data Analytics, ICBDA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9780738131672
Publication statusPublished - 2021 Mar 5
Event6th IEEE International Conference on Big Data Analytics, ICBDA 2021 - Xiamen, China
Duration: 2021 Mar 52021 Mar 8

Publication series

Name2021 IEEE 6th International Conference on Big Data Analytics, ICBDA 2021


Conference6th IEEE International Conference on Big Data Analytics, ICBDA 2021


  • POI recommendation
  • acceleration
  • clustering
  • location-based social networks
  • recommendation system

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Science Applications


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