Multi-hop Multi-view Memory Transformer for Session-based Recommendation

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xingrui Zhuo, Shengsheng Qian, Jun Hu, Fuxin Dai, Kangyi Lin, Gongqing Wu
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引用次数: 0

Abstract

A Session-Based Recommendation (SBR) seeks to predict users’ future item preferences by analyzing their interactions with previously clicked items. In recent approaches, Graph Neural Networks (GNNs) have been commonly applied to capture item relations within a session to infer user intentions. However, these GNN-based methods typically struggle with feature ambiguity between the sequential session information and the item conversion within an item graph, which may impede the model's ability to accurately infer user intentions. In this paper, we propose a novel Multi-hop Multi-view Memory Transformer (\(\rm{M^{3}T}\)) to effectively integrate the sequence-view information and relation conversion (graph-view information) of items in a session. First, we propose a Multi-view Memory Transformer (\(\rm{M^{2}T}\)) module to concurrently obtain multi-view information of items. Then, a set of trainable memory matrices are employed to store sharable item features, which mitigates cross-view item feature ambiguity. To comprehensively capture latent user intentions, a Multi-hop \(\rm{M^{2}T}\) (\(\rm{M^{3}T}\)) framework is designed to integrate user intentions across different hops of an item graph. Specifically, a k-order power method is proposed to manage the item graph to alleviate the over-smoothing problem when obtaining high-order relations of items. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our method.

基于会话推荐的多跳多视图内存转换器
基于会话的推荐(SBR)旨在通过分析用户与之前点击过的项目之间的互动来预测用户未来的项目偏好。在最近的方法中,图形神经网络(GNN)通常被用于捕捉会话中的项目关系,以推断用户意图。然而,这些基于图神经网络的方法通常难以解决连续会话信息与项目图内项目转换之间的特征模糊性问题,这可能会妨碍模型准确推断用户意图的能力。在本文中,我们提出了一种新颖的多跳多视图记忆转换器(Multi-hop Multi-view Memory Transformer),以有效整合会话中项目的序列视图信息和关系转换(图视图信息)。首先,我们提出了一个多视图记忆转换器(Multi-view Memory Transformer)模块来并发获取项目的多视图信息。然后,采用一组可训练的记忆矩阵来存储可共享的项目特征,从而减轻跨视角项目特征的模糊性。为了全面捕捉潜在用户意图,我们设计了一个多跳(\rm{M^{2}T}\)(\(\rm{M^{3}T}\))框架来整合项目图中不同跳的用户意图。具体来说,我们提出了一种 k 阶幂方法来管理项目图,以缓解在获取项目高阶关系时的过度平滑问题。在三个真实世界数据集上进行的广泛实验证明了我们方法的优越性。
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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