An improved deep sequential model for context-aware POI recommendation

被引:0
|
作者
Tipajin Thaipisutikul
Ying-Nong Chen
机构
[1] Mahidol University,Faculty of Information and Communication Technology
[2] National Central University,Center for Space and Remote Sensing Research
[3] National Central University,Department of Computer Science and Information Engineering
来源
关键词
Deep learning; Sequential behavior; Context awareness; User preference; Next POI recommendation;
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中图分类号
学科分类号
摘要
Nowadays, Location-based social networks (LSBNs) have been pervasive in our everyday lives since mobile users can share their experiences on point-of-interest (POIs) with friends anytime at hand. Currently, the recurrent neural network methods (RNN) have shown promising results for the next POI prediction task. However, they fail to incorporate important spatial-temporal contextual factors into the model learning process to distill the relevant long-term user preferences to the current user preference. Besides, traditional RNN-based models could only capture the consecutive check-ins relationship, which can obscure the complex motivations behind users' decisions in real-world scenarios. Therefore, in this paper, we propose An Improved Deep Sequential Model for Context-Aware POI Recommendation (SCR) for the next POI recommendation. Specifically, we model the user's short-term preference by a self multi-head attentive aggregation layer to capture the relationship of non-consecutive POIs under complex situations and aggregate all POI representations into a user's fine-grained short-term preference. Also, we model the users' long-term preferences by newly proposed three context-aware layers to discover the relevant past sessions on the current sessions based on temporal and spatial contexts. Finally, both users' short-and-long-term preferences are collaboratively fused for the final next POI prediction in a unified framework. The experimental results on two widely public POI datasets reveal the substantial enhancement of our proposed method over several state-of-the-art baselines from all evaluation metrics. Also, through the case study, we demonstrate the model's capability in delivering the interpretable recommendation results to the LSBN users.
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页码:1643 / 1668
页数:25
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