摘要
Session-based recommendation aims to predict user preferences based on anonymous behavior sequences.Recent research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns,which has achieved significant results.However,most existing studies only consider individual items in a session and do not extract information from continuous items,which can easily lead to the loss of information on item transition relationships.Therefore,this paper proposes a session-based recommendation algorithm(SGT)based on Gated Recurrent Unit(GRU)and Transformer,which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined way.By combining short-term sessions and long-term behavior,user dynamic preferences are captured.Extensive experiments were conducted on three session-based recommendation datasets,and compared to the baseline methods,both the recall rate Recall@20 and the mean reciprocal rank MRR@20 of the SGT algorithm were improved,demonstrating the effectiveness of the SGT method.
基金
supported by the Scientific Research Basic Ability Enhancement Program for Young and Middle-aged Teachers of Guangxi Higher Education Institutions,“Research on Deep Learning-based Recommendation Model and its Application”(Project No.2019KY0867)
Guangxi Innovation-driven Development Special Project(Science and Technology Major Special Project)
“Key Technology of Human-Machine Intelligent Interactive Touch Terminal Manufacturing and Industrial Cluster Application”(Project No.Guike AA21077018)
“Touch display integrated intelligent touch system and industrial cluster application”(Project No.:Guike AA21077018-2)
National Natural Science Foundation of China(Project No.:42065004).