Fu-Rec:Multi-Task Learning Recommendation Model Fusing Neighbor-Discrimination and Self-Discrimination  

作　　者：ZHENG Sirui HUANG Bo LIU Jin ZENG Guohui YIN Ling LI Zhi SUN Tie 

机构地区：[1]School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201600,China [2]School of Computer,Wuhan University,Wuhan 430072,Hubei,China [3]School of Computer Science and Engineering,Guangxi Normal University,Guilin 541004,Guangxi,China [4]AIoT Manufacturing Solutions Technology Co.,Ltd.,Hefei 230000,Anhui,China

出　　处：《Wuhan University Journal of Natural Sciences》2024年第2期134-144,共11页武汉大学学报（自然科学英文版）

基　　金：Supported by the Scientific and Technological Innovation 2030-Major Project of New Generation Artificial Intelligence(2020AAA0109300);Science and Technology Commission of Shanghai Municipality(21DZ2203100);2023 Anhui Province Key Research and Development Plan Project-Special Project of Science and Technology Cooperation(2023i11020002)。

摘　　要：In recent years,self-supervised learning has achieved great success in areas such as computer vision and natural language processing because it can mine supervised signals from unlabeled data and reduce the reliance on manual labels.However,the currently generated self-supervised signals are either neighbor discrimination or self-discrimination,and there is no model to integrate neighbor discrimination and self-discrimination.Based on this,this paper proposes Fu-Rec that integrates neighbor-discrimination contrastive learning and self-discrimination contrastive learning,which consists of three modules:(1)neighbor-discrimination contrastive learning,(2)selfdiscrimination contrastive learning,and(3)recommendation module.The neighbor-discrimination contrastive learning and selfdiscrimination contrastive learning tasks are used as auxiliary tasks to assist the recommendation task.The Fu-Rec model effectively utilizes the respective advantages of neighbor-discrimination and self-discrimination to consider the information of the user’s neighbors as well as the user and the item itself for the recommendation,which results in better performance of the recommendation module.Experimental results on several public datasets demonstrate the effectiveness of the Fu-Rec proposed in this paper.

关 键 词：self-supervised learning recommendation system contrastive learning multi-task learning 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]