计算机工程与应用2024，Vol.60Issue(11)：75-83,9.DOI:10.3778/j.issn.1002-8331.2303-0168
融合全局特征的时空网络兴趣点推荐算法
Spatio-Temporal Network Interest Point Recommendation Algorithm Fusing Global Features
摘要
Abstract
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.关键词
兴趣点推荐/门控循环单元/关联图神经网络/自注意力机制Key words
point of interest(POI)recommendation/gated recurrent unit/relational graph convolutional networks/self-attentive mechanism分类
信息技术与安全科学引用本文复制引用
李鹏飞,贺洋,毋建宏..融合全局特征的时空网络兴趣点推荐算法[J].计算机工程与应用,2024,60(11):75-83,9.基金项目
国家社科基金后期资助重点项目(21FGLA004) （21FGLA004）
陕西省社会科学基金(2019D038) （2019D038）
陕西省教育厅科研计划项目(21JP116) （21JP116）
西安市科技计划项目(22NYYF061) （22NYYF061）
陕西省科技创新团队(2023-CX-TD-13). （2023-CX-TD-13）
