融合全局特征的时空网络兴趣点推荐算法OA北大核心CSTPCD

Recommendation of point of interest(POI)is one of the most popular topics in location-based social network(LBSN).The existing recommendation methods do not fully consider the deep influence of the spatial and temporal inter-vals between sequences of check-in data on the recommended sequences.They ignore the global information in the check-in data and focus on the local preferences in the recent check-in sequence of a single user.To address these problems,this paper proposes a global feature fusion based spatiotemporal network(GSTN)interest point recommendation algorithm.The method uses graph neural networks to obtain global features of the heterogeneous network graph of check-in data,and incorporates spatiotemporal gating into the traditional gating structure,fuses global features to model users'mobile behavior,and then introduces a self-attentive mechanism to learn user preference vector representation.Finally,the experi-ments are carried out on two real datasets.The experiments show that the proposesd approach outperforms similar algo-rithms in terms of recommendation performance and verifies the effectiveness of the algorithm.