Abstract
In recent years,self-supervised learning has achieved great success in areas such as computer vision and natural language pro-cessing because it can mine supervised signals from unlabeled data and reduce the reliance on manual labels.However,the currently gener-ated self-supervised signals are either neighbor discrimination or self-discrimination,and there is no model to integrate neighbor discrimi-nation 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)self-discrimination contrastive learning,and(3)recommendation module.The neighbor-discrimination contrastive learning and self-discrimination contrastive learning tasks are used as auxiliary tasks to assist the recommendation task.The Fu-Rec model effectively uti-lizes 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.Experi-mental results on several public datasets demonstrate the effectiveness of the Fu-Rec proposed in this paper.
基金项目
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)
NETL
NSTL
万方数据