计算机应用研究2024，Vol.41Issue(11)：3382-3388,7.DOI:10.19734/j.issn.1001-3695.2024.03.0092
基于GT模型的多编码下一个兴趣点推荐模型
Multi-coding next point of interest recommendation model based on GT model
摘要
Abstract
Next point of interest(POI)recommendation is a hot topic in the field of recommendation algorithms,which aims at recommending the suitable next locations for users.Recent research has significantly improved performance by simulating user interactions with POIs and the transitions between POIs using graph and sequence methods.However,existing models still have issues that need to be addressed.In response to the limitations of current next POI recommendation models,particularly in how to fully capture both global and local information on the user-POI interaction graph,and in alleviating the oversmoothing characteristics of graph neural networks that lead to information loss on the graph,this paper proposed a multi-coding network based on the graph Transformer model for recommending the next POI.Firstly,it jointly encoded global,local,and relative information on the user-POI interaction graph from the perspectives of position and structure.Then,the graph embeddings pro-duced by this encoding were updated through graph Transformer network layers,which refreshed the information of nodes and edges on the graph.Finally,predictions were generated through MLP network layers.The MCGT model was empirically tested on two public datasets,Gowalla and TKY.The results show that at least a 3.79％improvement in recall and NDCG metrics on the Gowalla dataset and at least a 2.5％improvement on the TKY dataset,thus proving the reasonableness and effectiveness of MCGT.关键词
下一个兴趣点推荐/多编码/全局信息/局部信息/相对信息/图TransformerKey words
next point of interest recommendation/multi-encoding/global information/local information/relative informa-tion/graph Transformer分类
信息技术与安全科学引用本文复制引用
王永贵,张小锐..基于GT模型的多编码下一个兴趣点推荐模型[J].计算机应用研究,2024,41(11):3382-3388,7.基金项目
国家自然科学基金面上项目(61772249) （61772249）
辽宁省教育厅科学研究经费资助项目(LJKZ0355) （LJKZ0355）
