{"title":"Multi-hop Multi-view Memory Transformer for Session-based Recommendation","authors":"Xingrui Zhuo, Shengsheng Qian, Jun Hu, Fuxin Dai, Kangyi Lin, Gongqing Wu","doi":"10.1145/3663760","DOIUrl":null,"url":null,"abstract":"<p>A <b>S</b>ession-<b>B</b>ased <b>R</b>ecommendation (SBR) seeks to predict users’ future item preferences by analyzing their interactions with previously clicked items. In recent approaches, <b>G</b>raph <b>N</b>eural <b>N</b>etworks (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 <b>M</b>ulti-hop <b>M</b>ulti-view <b>M</b>emory <b>T</b>ransformer (\\(\\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 <b>M</b>ulti-view <b>M</b>emory <b>T</b>ransformer (\\(\\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 <b>M</b>ulti-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.</p>","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"7 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3663760","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.