文献类型：期刊文献

英文题名：Point-of-interest recommendation based on the spatial-temporal graph

作者：Li, Mengyue[1];Li, Fei[1];Wang, Zhanquan[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

年份：2024

卷号：20

期号：6

起止页码：585

外文期刊名：INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS

收录：EI(收录号：20244617370364);WOS:【ESCI(收录号:WOS:001352749500001)】；

语种：英文

外文关键词：Graph neural networks; POI recommendation; Spatial-temporal information

摘要：Purpose - Point-of-interest (POI) recommendation techniques play a crucial role in mitigating information overload and delivering tailored services. To address limitations in conventional POI recommendation systems, constrained by sparse user-POI interactions and incomplete consideration of temporal dynamics, POI recommendation based on the spatial-temporal graph (STG-POI) is proposed. Design/methodology/approach - Spatial-temporal sequence graphs from geographical locations and user interaction history data are constructed, which are used to mine spatial-temporal sequence information. Using the data fi ltered by the band-pass fi lter, graph neural networks with distance-awareness and sequence- awareness are applied to capture high-order spatial-temporal connections within diverse graph topologies. The model leverages contrastive learning for self-supervised disentanglement of graph representations, providing self-supervised signals for sequential and geographical intent perception, thereby achieving more precise POI personalization.
Findings - Compared to the baseline model GSTN, experiments on the Foursquare and Gowalla data sets reveal that STG-POI improves testing AUC by 2.0%, 2.1%, 2.0% and decreases logloss by 1.9%, 3.3%, 0.3%, respectively. These results indicate the model's effectiveness in capturing spatial-temporal information, surpassing mainstream POI recommendation baseline models.
Originality/value - This approach constructs a dual graph from user interaction data, harnessing sequential and geographical information as self-supervised signals. It yields decoupled representations of these influences, offering a comprehensive insight into user behaviors and preferences within location-based social networks, thus enhancing recommendation accuracy and interpretability. This approach addresses the challenge in graph convolutional network where only rough and smooth features are conducive to recommendation by using band-pass fi lters to significantly reduce computational complexity, thereby enhancing recommendation speed by fi ltering out noise data that does not contribute to recommendation performance. Experimental results indicate that this model surpasses current mainstream approaches in POI recommendation tasks, effectively integrating both geographical and temporal features.