基于全局一致性增强的多偏好会话推荐模型

doi:10.16451/j.cnki.issn1003-6059.202406003

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Abstract Session-based recommendation aims to predict the next item which a user is likely to interact with based on an anonymous session. However, existing session-based recommendation methods based on graph neural networks underutilize the global information. To address this issue, a global consistency augmented multi-preference session-based recommendation model(GCAM) is proposed. Firstly, a consistent global graph is constructed through the shortest path routing algorithm. The consistency of global information is ensured by capturing reliable item relationships and filtering out unreliable item relationships. Secondly, a multi-preference label smoothing strategy is applied to mine collaborative information from historical sessions to soften labels, and thereby the label can fit the true user preferences. Extensive experiments on three different datasets demonstrate the superiority of GCAM.

Key words： Session-Based Recommendation Multi-preference Learning Self-Supervised Learning Global Consistency Augmentation

Received: 14 March 2024

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.62076046,62006034)

Corresponding Authors: LIN Hongfei, Ph.D., professor. His research interests include natural language processing, affective computing, social media processing and information retrieval.

About author:: WU Jiangming, Master student. His research interests include recommendation systems and natural language processing. ZHANG Xiaokun, Ph.D. candidate. His research interests include data mining and recommendation systems. XU Bo, Ph.D., associate professor. His research interests include information retrieval and natural language processing. YANG Liang, Ph.D., associate professor. His research interests include natural language processing.

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	WU Jiangming
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Cite this article:

WU Jiangming,ZHANG Xiaokun,XU Bo等. Global Consistency Augmented Multi-preference Session-Based Recommendation Model[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(6): 513-524.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202406003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I6/513

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