Time Distribution Based Diversified Point of Interest Recommendation

Fan Mo, Huida Jiao, Hayato Yamana

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

Abstract

In location-based social networks (LBSNs), personalized point-of-interest (POI) recommendation helps users mine their interests and find new locations conveniently and quickly. It is one of the most important services to improve users' quality of life and travel. Most POI recommendation systems devoted to improve accuracy, however in recent years, diversity of POI recommendations, such as categorical and geographical diversity, receives much attention because a single type of POIs easily causes loss of users' interest. Different from previous diversity related recommendations, in this paper, we focus on visiting time of POI- A unique attribute of the interaction between users and POIs. Users usually have different active visiting time patterns and different frequently visiting POIs depending on time. If a set of proper visiting times of recommended POIs concentrates on a small range of time, the user might be unsatisfied because they cannot cover whole of the user's active time range that results in inappropriateness for the user to visit those POIs. To solve this problem, we propose a new concept-time diversity and a time distribution based recommendation method to improve time diversity of recommended POIs. Our experimental result with Gowalla dataset shows our proposed method effectively improves time diversity 25.9% compared with USG with only 7.9% accuracy loss.

Original languageEnglish
Title of host publication2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-44
Number of pages8
ISBN (Electronic)9781728160245
DOIs
Publication statusPublished - 2020 Apr
Event5th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020 - Chengdu, China
Duration: 2020 Apr 102020 Apr 13

Publication series

Name2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020

Conference

Conference5th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020
CountryChina
CityChengdu
Period20/4/1020/4/13

Keywords

  • Location-base social networks
  • POI recommendation
  • recommendation system
  • time diversity

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Optimization
  • Modelling and Simulation

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  • Cite this

    Mo, F., Jiao, H., & Yamana, H. (2020). Time Distribution Based Diversified Point of Interest Recommendation. In 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020 (pp. 37-44). [9095741] (2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCCBDA49378.2020.9095741