Point of Interest Recommendation Methods for Fused Temporal Gated Graph Neural Networks

doi:10.3778/j.issn.1002-8331.2305-0262

Abstract

Abstract: Most of the existing points of interest (point of interest, POI) recommendation systems ignore the sequential behavior mode in the user check-in sequence and the influence of users’ personalized preference on POI recommendation, which leads to the low performance of POI recommendation system and the unreliable recommendation results, and thus affects the user experience. To solve the above problems, a point of interest recommendation method is proposed to fuse the temporal gated graph neural network. Firstly, use temporal gated graph neural network (TGGNN) to learn the POI embedding. Secondly, use attention mechanism to capture long-term preferences. Then, integrate the latest preferences and real-time preferences to capture short-term preferences. Finally, the recommendation scores of the candidate POIs are calculated by combining the users’ long-term and short-term preferences and the POI recommendation is performed for the user according to the score. The experimental results show that compared with the existing methods, the proposed method can significantly improve the recall rate and average reciprocal ranking, so it can achieve good recommendation effect and has a good application prospect.

Key words: point of interest recommendation, attention mechanism, temporal gated graph neural network, window pooling, real-time preference

摘要： 现有的大多数兴趣点（point of interest， POI）推荐系统由于忽略了用户签到序列中的顺序行为模式，以及用户的个性化偏好对于POI推荐的影响，导致POI推荐系统性能较低，推荐结果不可靠，进而影响用户体验。为了解决上述问题，提出一种融合时序门控图神经网络的兴趣点推荐方法。运用时序门控图神经网络（temporal gated graph neural network，TGGNN）学习POI embedding；采用注意力机制捕获用户的长期偏好；通过注意力机制融合用户的最新偏好和实时偏好，进而捕获用户的短期偏好。通过自适应的方式结合用户的长期和短期偏好，计算候选POI的推荐得分，并根据得分为用户进行POI推荐。实验结果表明，与现有方法相比，该方法在召回率和平均倒数排名这两项指标上均有较为明显的提升，因此可以取得很好的推荐效果，具有良好的应用前景。

TANG Hong, LIU Bin, ZHANG Jing, JIN Zhezheng. Point of Interest Recommendation Methods for Fused Temporal Gated Graph Neural Networks[J]. Computer Engineering and Applications, 2024, 60(16): 124-132.

唐宏, 刘斌, 张静, 金哲正. 融合时序门控图神经网络的兴趣点推荐方法[J]. 计算机工程与应用, 2024, 60(16): 124-132.

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