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Attentive sequential model based on graph neural network for next poi recommendation

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Abstract

With the rapid development of Information Technology, there exist massive amounts of data available on the Internet, which result in a severe information overload problem. Especially, it becomes more and more challenging but necessary to help users find the contents or services that they really need. To address the problem mentioned above, recommender systems have been developed to exploit user’s historical behavior data and provide personalized services for promoting customer experiences in many fields, such as Point of Interest (POI) applications, multimedia services, and e-commerce websites. Specifically, in POI recommendation, user’s next check-in behaviors depend on both long- and short-term preferences. However, traditional recommendation methods often ignore the dynamic changes of user’s short-term preferences over time, which limits their performance. Besides, many existing methods cannot fully exploit the complex correlations and transitions between POI in check-ins sequences. In this paper, we propose an A ttentive S equential model based on G raph N eural N etwork (ASGNN) for accurate next POI recommendation. Specifically, ASGNN firstly models user’s check-in sequences as graphs and then use Graph Neural Networks (GNN) to learn the informative low-dimension latent feature vectors (embeddings) of POIs. Secondly, a personalized hierarchical attention network is adopted to exploit complex correlations between users and POIs in check-in sequences and capture user’s long- and short-term preferences. Finally, we perform the next POI recommendation via leveraging user’s long- and short-term preferences obtained from their behavior sequences with ASGNN. Extensive experiments are conducted on three real-world check-in datasets, and the results demonstrate that the proposed model ASGNN outperforms baselines, including some state-of-the-art methods.

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  1. https://meituan.todayir.com/attachment/202003301717261783547356_en.pdf

  2. https://snap.stanford.edu/data/loc-gowalla.html

  3. https://sites.google.com/site/yangdingqi/home/foursquare-dataset

  4. https://snap.stanford.edu/data/loc-brightkite.html

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Authors and Affiliations

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

    Dongjing Wang, Xingliang Wang & Dongjin Yu

  2. School of Computer & Computing Science, Zhejiang University City College, Zhejiang, China

    Zhengzhe Xiang

  3. College of Computer Science and Technology, Zhejiang University, Zhejiang, China

    Shuiguang Deng

  4. Advanced Analytics Institute, University of Technology, Sydney, Australia

    Guandong Xu

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  1. Dongjing Wang
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  2. Xingliang Wang
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Correspondence to Dongjin Yu.

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This research was supported by Natural Science Foundation of Zhejiang Province under No.LQ20F020015, the Fundamental Research Funds for the Provincial University of Zhejiang by Hangzhou Dianzi University under No.GK199900299012-017, and Zhejiang Provincial Key Science and Technology Program Foundation under No.2020C01165.

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Wang, D., Wang, X., Xiang, Z. et al. Attentive sequential model based on graph neural network for next poi recommendation. World Wide Web 24, 2161–2184 (2021). https://doi.org/10.1007/s11280-021-00961-9

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