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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摘要基于会话的推荐旨在根据一组匿名会话预测用户下一个可能交互的物品.现有的基于图神经网络的会话推荐模型对全局信息的利用不足.为此,文中提出基于全局一致性增强的多偏好会话推荐模型(Global Consistency Augmented Multi-preference Session-Based Recommendation Model, GCAM).首先,在利用全局信息时,通过最短路径搜索算法构建一致性全局图,捕捉强依赖的物品关系,过滤不可靠的物品关系,从而保证全局信息的一致性.然后,应用一种多偏好标签平滑策略,从历史会话中充分挖掘协同信息,对标签进行平滑化,拟合用户偏好的真实分布.在3个数据集上的大量实验表明GCAM的优越性.

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关键词 ：会话推荐, 多偏好学习, 自监督学习, 全局一致性增强

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

收稿日期: 2024-03-14

ZTFLH:

TP 391

基金资助:国家自然科学基金项目(No.62076046,62006034)资助

通讯作者: 林鸿飞,博士,教授,主要研究方向为自然语言处理、情感计算、社交媒体处理、信息检索等.E-mail:hflin@dlut.edu.cn.

作者简介: 吴江铭,硕士研究生,主要研究方向为推荐系统、自然语言处理等.E-mail:orangetc510@gmail.com. 张晓堃,博士研究生,主要研究方向为数据挖掘、推荐系统等.E-mail:dawnkun1993@gmail.com. 徐博,博士,副教授,主要研究方向为信息检索、自然语言处理等.E-mail:xubo@dlut.edu.cn. 杨亮,博士,副教授,主要研究方向为自然语言处理等.E-mail:liang@dlut.edu.cn.

引用本文:

吴江铭, 张晓堃, 徐博, 杨亮, 林鸿飞. 基于全局一致性增强的多偏好会话推荐模型[J]. 模式识别与人工智能, 2024, 37(6): 513-524. WU Jiangming, ZHANG Xiaokun, XU Bo, YANG Liang, LIN Hongfei. Global Consistency Augmented Multi-preference Session-Based Recommendation Model. Pattern Recognition and Artificial Intelligence, 2024, 37(6): 513-524.

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