计算机科学与探索2024，Vol.18Issue(3)：768-779,12.DOI:10.3778/j.issn.1673-9418.2211090
融合物品转换关系和时序信息的会话推荐算法
Session Recommendation Algorithm Combining Item Transition Relations and Time-Order Information
摘要
Abstract
Aiming at the problem that the existing graph neural network session recommendation algorithm ignores all kinds of auxiliary information,which leads to the inability to accurately model the session sequence,a session recommendation algorithm combining the item transition relations and time-order information(RTSR)is proposed.Firstly,the shortest path sequence between any two nodes is obtained by using the graph network structure,which is encoded as the item transition relations between corresponding items through the gated recurrent unit(GRU),and then the global dependency information of the session is captured from the perspective of the graph by combining the self-attention mechanism.At the same time,a lossless graph coding scheme is designed to alleviate the problem of information loss in the process of session graph coding.The scheme quantifies the time-order information in the session sequence reasonably,and takes it as the weight of the edges in the session graph,and then combines the gated graph sequence neural network to obtain the local dependency information of the session.Finally,with linear combi-nation of global dependency information and local dependency information,and in combination with reverse posi-tion information,the user's preference for item is finally generated,and the recommendation list is given.The per-formance comparison experiment with mainstream models such as SR-GNN,GC-SAN and GCE-GNN on the public benchmark datasets Gowalla and Diginetica shows that RTSR improves at least 6.13%and 1.58%in average recip-rocal ranking respectively,and the recommendation accuracy is also improved accordingly.关键词
图神经网络/会话推荐/最短路径序列/时序信息/反向位置信息Key words
graph neural network/session recommendation/shortest path sequence/time-order information/re-verse position information分类
信息技术与安全科学引用本文复制引用
吴文政,卢先领..融合物品转换关系和时序信息的会话推荐算法[J].计算机科学与探索,2024,18(3):768-779,12.基金项目
国家自然科学基金(61773181).This work was supported by the National Natural Science Foundation of China(61773181). （61773181）
