S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, Ji-Rong Wen
Abstract
Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation.
Topics & Concepts
Computer scienceContext (archaeology)Artificial intelligenceMutual informationSequence (biology)Machine learningArtificial neural networkData miningDeep learningMaximizationContext modelInformation lossData modelingRecommender systemExpectation–maximization algorithmSensor fusionSequence learningData associationComplete informationTraining setAssociation (psychology)Recommender Systems and TechniquesTopic ModelingAdvanced Graph Neural Networks