About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings

Research Article

Context-Aware Point-of-Interest Recommendation Algorithm with Interpretability

Download(Requires a free EAI acccount)
241 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-030-30146-0_50,
        author={Guoming Zhang and Lianyong Qi and Xuyun Zhang and Xiaolong Xu and Wanchun Dou},
        title={Context-Aware Point-of-Interest Recommendation Algorithm with Interpretability},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={8},
        keywords={Point-of-interest recommendation Interpretability Location based social network},
        doi={10.1007/978-3-030-30146-0_50}
    }
    
  • Guoming Zhang
    Lianyong Qi
    Xuyun Zhang
    Xiaolong Xu
    Wanchun Dou
    Year: 2019
    Context-Aware Point-of-Interest Recommendation Algorithm with Interpretability
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-30146-0_50
Guoming Zhang,*, Lianyong Qi1,*, Xuyun Zhang2,*, Xiaolong Xu3,*, Wanchun Dou4,*
  • 1: Qufu Normal University
  • 2: The University of Auckland
  • 3: Nanjing University of Information Science and Technology
  • 4: Nanjing University
*Contact email: kelvinzhang@smail.nju.edu.cn, lianyongqi@gmail.com, xuyun.zhang@auckland.ac.nz, njuxlxu@gmail.com, douwc@nju.edu.cn

Abstract

With the rapid development of mobile Internet, smart devices, and positioning technologies, location-based social networks (LBSNs) are growing rapidly. In LBSNs, point-of-interest (POI) recommendation is a crucial personalized location service that has become a research hotspot. To address extreme sparsity of user check-in data, a growing line of research exploits spatial-temporal information, social relationship, content information, popularity, and other factors to improve recommendation performance. However, the temporal and spatial transfers of user preferences are seldom mentioned in existing works, and interpretability, which is an important factor to enhance credibility of recommendation result, is overlooked. To cope with these issues, we propose a context-aware POI recommendation framework, which integrates users’ long-term static and time-varying preferences to improve recommendation performance and provide explanations. Experimental results over two real-world LBSN datasets demonstrate that the proposed solution has better performance than other advanced POI recommendation approaches.

Keywords
Point-of-interest recommendation Interpretability Location based social network
Published
2019-08-19
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-30146-0_50
Copyright © 2019–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL