SGT:Session-based Recommendation with GRU and Transformer  

作　　者：Lingmei Wu Liqiang Zhang Xing Zhang Linli Jiang Chunmei Wu 

机构地区：[1]School of Mathematics&Computer Science,Guangxi Science&Technology Normal University,Laibin,Guangxi,546199,China

出　　处：《Journal of Computer Science Research》2023年第2期37-51,共15页计算机科学研究（英文）

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).

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.

关 键 词：Recommender system Gated recurrent unit Transformer Session-based recommendation Graph neural networks 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]