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Leveraging contextual influence and user preferences for point-of-interest recommendation

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Abstract

The effective Point-of-Interest (POI) recommendation can significantly assist users to find their preferred POIs and help POI owners to attract more customers. As a result, a variety of methods have been proposed to tackle the issue of POI recommendation recently. However, it is still very difficult to precisely model the strong correlations between the POIs visited by the user and the POIs to be visited next, which leads to the poor performance of POI recommendation. In this paper, we propose a context- and preference- aware model (CPAM) to incorporate both contextual influence and user preferences into POI recommendation. Firstly, we design a Skip-Gram based POI Embedding Model (SG-PEM) to capture the contextual influence of POIs and learn the vector representation (embedding) of POIs from visiting sequences. The users’ preferences for the target POIs are obtained from the learned embeddings via similarity metric. Secondly, for the implicit feedback information contained in the check-in data, we use the Logistic Matrix Factorization (LMF) algorithm to model the users’ personalized preferences for POI. Finally, we unify SG-PEM and LMF as the CPAM model to perform personalized recommendation by leveraging contextual influence and user preferences. The experimental results on two real-world datasets of Foursquare and Gowalla show that the proposed model outperforms the state-of-the-art baselines.

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Notes

  1. http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/

  2. iGLSR is evaluated only on Gowalla as we do not have access to the social data of the Foursquare.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Dongjin Yu, Wenbo Wanyan & Dongjing Wang

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  1. Dongjin Yu
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  2. Wenbo Wanyan
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  3. Dongjing Wang
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Correspondence to Dongjing Wang.

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This research is supported by Zhejiang Provincial Natural Science Foundation of China under No. LQ20F020015, and the Fundamental Research Funds for the Provincial University of Zhejiang under No. GK199900299012-017.

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Yu, D., Wanyan, W. & Wang, D. Leveraging contextual influence and user preferences for point-of-interest recommendation. Multimed Tools Appl 80, 1487–1501 (2021). https://doi.org/10.1007/s11042-020-09746-0

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