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Next location recommendation: a multi-context features integration perspective

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Abstract

Next location recommendation aims to mine users’ historical trajectories to predict their potentially preferred locations in the next moment. Although previous studies have explored the idea of incorporating location or social contextual information for recommendation, they still suffer from several major limitations: (1) not fully considering the semantic associations between locations, (2) not considering the heterogeneity in preferences of socially linked users, (3) not fully utilizing contextual information from distinctive sources to further improve the recommendation performance. In this paper, we propose a novel multi-context-based next location recommendation model that incorporates location context, trajectory context, and social context to obtain comprehensive users’ preferences while allowing for interactions between contexts. Specifically, we first develop an efficient method combining both high-order location graphs and location semantic graphs to characterize subtle associations between locations. Then we explore the social contextual information and introduce the location subgraph which considers heterogeneous preferences among friends. Finally, we use the LSTM and geo-dilated LSTM to capture the spatio-temporal associations between users’ trajectories and integrate various contextual information to improve model performance. Extensive experiments on three real datasets show that our model has superior results in the next location recommendation task over other baselines.

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Funding

The National Natural Science Foundation of China (71802068, 72271084, 72071069); The University Synergy Innovation Program of Anhui Province (GXXT-2021-004)

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, China

    Xuemei Wei, Chunli Liu & Yezheng Liu

  2. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

    Chunli Liu

  3. National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, China

    Yezheng Liu

  4. Montclair State University, Montclair, NJ, USA

    Yang Li

  5. School of Data Science, University of Science and Technology of China, Hefei, China

    Kai Zhang

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  1. Xuemei Wei
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Contributions

Xuemei Wei designed the model and completed the experiments, Chunli Liu participated in the model design and wrote the main manuscript text, Yezheng Liu and Yang Li wrote the main manuscript text, and Kai Zhang completed the data processing and prepared Figures 1-4. All authors reviewed the manuscript.

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Correspondence to Chunli Liu.

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All data can be found at: https://sites.google.com/site/yangdingqi/home/foursquare-dataset

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This article belongs to the Topical Collection: Special Issue on Spatiotemporal Data Management and Analytics for Recommend Guest Editors: Shuo Shang, Xiangliang Zhang and Panos Kalnis

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Wei, X., Liu, C., Liu, Y. et al. Next location recommendation: a multi-context features integration perspective. World Wide Web 26, 2051–2074 (2023). https://doi.org/10.1007/s11280-022-01126-y

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