传统兴趣点（point of interest,POI）推荐方法对用户和POI的关联关系挖掘不充分，无法全面捕捉用户偏好；基于图增强的推荐方法虽能挖掘关联关系，却易引入噪声，降低推荐性能。针对这些问题，本文提出了结合通用轨迹图和多偏好的POI推荐方法。首先构建了用户与POI的带权二部图，利用图卷积网络捕捉用户和POI的交互关系，学习用户兴趣偏好；利用兴趣偏好完成用户聚类，进而构建同类型用户通用轨迹图，减少噪声信息影响；利用图卷积网络捕捉同类型用户的群体特征，丰富特征表示。其次，将群体特征与用户当前轨迹中时间类别感知信息、时空上下文信息相结合，利用Transformer挖掘用户的深层行为偏好。再次，构造非线性加性函数并将兴趣偏好和行为偏好动态组合，全面捕捉用户偏好，完成POI推荐。最后，在真实数据集上验证了本文方法的有效性。
Traditional point of interest (POI) recommendation methods often fail to fully mine the relationships between users and POIs, resulting in limited capacity to capture user preferences. Although graph enhancement method offers improved relational modeling, it is susceptible to noise, which reduces the recommendation precision. To address these challenges, this paper proposes a POI recommendation method named combining universal trajectory maps and multiple preferences. Firstly, a weighted bipartite graph between users and POIs is constructed. Graph convolutional network (GCN) is used to extract the interactive relationship between users and POIs to learn users’ interest preferences. Users are clustered using the obtained interest preferences. The general trajectory maps of the same type of users are built to reduce the impact of noise information. GCN is further used to mine the group features of different types of users and enrich feature representation. Secondly, the group features are combined with time-aware category information and spatio-temporal context from the user’s current trajectory. Transformer model is used to capture deep behavioral preferences. Finally, a non-linear additive function is used to dynamically combine interest preferences with current behavior preferences to fully capture user preferences and generate POI recommendations. The validity of the proposed method is verified on real data sets.
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