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Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

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  • Chonghuan Xu

    (School of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China
    Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China
    Academe of Zhejiang Culture Industry Innovation & Development, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Dongsheng Liu

    (School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Xinyao Mei

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top- K POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.

Suggested Citation

  • Chonghuan Xu & Dongsheng Liu & Xinyao Mei, 2021. "Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors," Mathematics, MDPI, vol. 9(21), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2673-:d:661716
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    References listed on IDEAS

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    1. Jun Zeng & Feng Li & Xin He & Junhao Wen, 2019. "Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation," International Journal of Web Services Research (IJWSR), IGI Global, vol. 16(4), pages 40-52, October.
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    Cited by:

    1. Bilin Zou & Chunhua Ju & Fuguang Bao & Ye Lai & Chonghuan Xu & Yiwen Zhu, 2022. "Exploring an Efficient Evolutionary Game Model for the Government–Enterprise–Public during the Double Carbon Policy in China," IJERPH, MDPI, vol. 19(8), pages 1-27, April.
    2. Xiaoyan Li & Shenghua Xu & Tao Jiang & Yong Wang & Yu Ma & Yiming Liu, 2022. "POI Recommendation Method of Neural Matrix Factorization Integrating Auxiliary Attribute Information," Mathematics, MDPI, vol. 10(19), pages 1-14, September.
    3. Guanghui Qiao & Liu Ding & Keheng Xiang & Bruce Prideaux & Jinyi Xu, 2022. "Understanding the Value of Tourism to Seniors’ Health and Positive Aging," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    4. Guanglan Zhou & Luyao Zhu, 2022. "Distribution Characteristics and Influencing Factors of Supply Chain Innovation Firms: A Case Study of Zhejiang Province," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    5. Chunhua Ju & Zhonghua Shen & Fuguang Bao & Pengtong Weng & Yihang Xu & Chonghuan Xu, 2022. "A Novel Credible Carbon Footprint Traceability System for Low Carbon Economy Using Blockchain Technology," IJERPH, MDPI, vol. 19(16), pages 1-16, August.
    6. Guanglan Zhou & Zhening Zhang & Yulian Fei, 2022. "How to Evaluate the Green and High-Quality Development Path? An FsQCA Approach on the China Pilot Free Trade Zone," IJERPH, MDPI, vol. 19(1), pages 1-17, January.

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