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CHA: Categorical Hierarchy-based Attention for Next POI Recommendation
Journal article   Peer reviewed

CHA: Categorical Hierarchy-based Attention for Next POI Recommendation

Hongyu Zang, Dongcheng Han, Xin Li, Zhifeng Wan and Mingzhong Wang
ACM Transactions on Information Systems, Vol.40(1), pp.1-22
2021
DOI: https://doi.org/10.1145/3464300
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https://doi.org/10.1145/3464300View
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Abstract

categorical hierarchy-based attention next POI recommendation next new POI recommendation
Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.

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Computer Science, Information Systems
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