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Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16–18, 2020, Proceedings, Part I

Research Article

RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context

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  • @INPROCEEDINGS{10.1007/978-3-030-67537-0_39,
        author={Jun Zeng and Haoran Tang and Xin He},
        title={RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I},
        proceedings_a={COLLABORATECOM},
        year={2021},
        month={1},
        keywords={Recommendation system Point of interest Region Context Collaborative filtering},
        doi={10.1007/978-3-030-67537-0_39}
    }
    
  • Jun Zeng
    Haoran Tang
    Xin He
    Year: 2021
    RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-67537-0_39
Jun Zeng1,*, Haoran Tang1, Xin He1
  • 1: School of Big Data and Software Engineering
*Contact email: zengjun@cqu.edu.cn

Abstract

In the past few years, mobile application has been innovated by leaps and bounds, which leads the prevalence of location-based social networks (LBSNs). Point of interest (POI) recommendation aims to recommend satisfactory locations to users in mobile environment and plays an important role in LBSNs. However, there are still two challenges to be solved. One is the data sparseness caused by users who just visit a few POIs. The other is that it’s hard to make reasonable explanation of recommendation from the perspective of real world. Hence, firstly we propose a region-based collaborative filtering to alleviate the data sparseness by clustering locations into regions. Secondly, we model the impact of two kinds of user contexts like geographical distance and POI category to make POI recommendation more reasonable. Finally, we present a joint model called RCFC which combines the two parts mentioned above. Results of experiments on two real-world datasets demonstrate the model we propose outperforms the popular recommendation algorithms and is more in line with the situation in real world.

Keywords
Recommendation system Point of interest Region Context Collaborative filtering
Published
2021-01-22
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-67537-0_39
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