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Fused matrix factorization with multi-tag, social and geographical influences for POI recommendation

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Abstract

We make plans to provid a point-of-interests (POI) recommendation method for location-based social networks (LBSNs) in the paper. LBSNs contain a special three layers network structure based on relevance of location information in the physical world. The available information of three layers of LBSN makes it possible to mine the internal correlations between different layers and to analze users’ preference on locations. We try to model the muti-tag, social and geographical influences separately form three layers of LBSN. We first model the mutli-tag influences via extracting a user-tag matrix from intial uer-POI matrix. Next, we introduce social regularization to model the social influences, Thirdly, we use a normalized function to model the geographical influences. Accordingly, we include multi-tag influences and fuse the social regularization, geographical influence into a famous matrix factorization(MF) framework. Finally, we conduct extensive performance evaluations on the large-scale Yelp datasets. As a result, the fusion framework outperforms other state-of-the-art approaches on recommendation.

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Acknowledgements

This work has been supported by the Fundamental Research Funds for the Central Universities, No. W18RC00010.

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Authors and Affiliations

  1. Department of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, China

    Zhiyuan Zhang, Yun Liu & Bo Shen

  2. School of Software, Beijing Jiaotong University, Beijing, China

    Zhenjiang Zhang

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  1. Zhiyuan Zhang
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  2. Yun Liu
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  3. Zhenjiang Zhang
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  4. Bo Shen
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Correspondence to Zhiyuan Zhang.

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This article belongs to the Topical Collection: Special Issue on Social Media and Interactive Technologies

Guest Editors: Timothy K. Shih, Lin Hui, Somchoke Ruengittinun, and Qing Li

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Zhang, Z., Liu, Y., Zhang, Z. et al. Fused matrix factorization with multi-tag, social and geographical influences for POI recommendation. World Wide Web 22, 1135–1150 (2019). https://doi.org/10.1007/s11280-018-0579-9

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  • DOI: https://doi.org/10.1007/s11280-018-0579-9

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