Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220100066-8.doi: 10.11896/jsjkx.220100066

• Big Data & Data Science • Previous Articles     Next Articles

Explainable Constraint Mechanism for Modeling Temporal Sentiment Memory in Sequential Recommendation

ZHENG Lin1, LIN Yixuan1, ZHOU Donglin1, ZHU Fuxi2   

  1. 1 College of Engineering,Shantou University,Shantou,Guangdong 515063,China;
    2 School of Computer Science,Wuhan University,Wuhan 430072,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:ZHENG Lin,born in 1984,Ph.D,Lecturer,master supervisor,is a member of ACM and CCF.His main research interests include explainable deep learning and recommender systems.
  • Supported by:
    National Natural Science Foundation of China(61902231),Guangdong Basic and Applied Basic Research Foundation(2023A1515011240,2020A1515010531) and Higher Education Special Project of Guangdong Education Science Planning(2021GXJK241).

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Abstract: In recent years,the research of sequential recommendation has developed rapidly in the recommendation field,existing methods are good at capturing users’ sequential behavior to achieve preference prediction.Among them,some advanced methods integrate users’ sentiment information to guide behavior mining.However,the advanced sentiment-based models do not consider mining relations between multi-category user sentiment sequences.Moreover,such methods cannot intuitively explain the contribution of temporal sentiments to user preferences.To make up for the above shortcomings,this paper first attempts to store temporal sentiments in the form of memory and impose constraints on them.Specifically,this research proposes two mechanisms including sentiment self-constraint and sentiment mutual-constraint to explore the associations between multiple categories of sentiments and assist user behaviors in completing sequential recommendations.Furthermore,the proposed memory framework is able to record users’ temporal sentiment attention,so that it can provide a certain degree of intuitive explanation on the basis of accurately predicting users’ temporal preference.Experimental results show that our approach outperforms existing state-of-the-art sequential methods,and it has better explainable effects than the sentiment-based sequential recommendation models.

Key words: Sequential recommendation, Temporal sentiment, Sentiment memory, Sentiment constraint, Explainable recommendation

CLC Number: 

  • TP311
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