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HOPE: a hybrid deep neural model for out-of-town next POI recommendation

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Abstract

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61872258, 61802273, Major project of natural science research in Universities of Jiangsu Province under grant number 20KJA520005.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Huimin Sun, Jiajie Xu, Wei Chen & Lei Zhao

  2. State Key Laboratory of Software Architecture (Neusoft Corporation), Shenyang, China

    Huimin Sun

  3. Faculty of SET, Swinburne University of Technology, Melbourne, Australia

    Rui Zhou & Chengfei Liu

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  1. Huimin Sun
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  2. Jiajie Xu
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  3. Rui Zhou
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  4. Wei Chen
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  6. Chengfei Liu
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Correspondence to Wei Chen.

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This article belongs to the Topical Collection: Special Issue on Explainability in the Web

Guest Editors: Guandong Xu, Hongzhi Yin, Irwin King, and Lin Li

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Sun, H., Xu, J., Zhou, R. et al. HOPE: a hybrid deep neural model for out-of-town next POI recommendation. World Wide Web 24, 1749–1768 (2021). https://doi.org/10.1007/s11280-021-00895-2

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