ACM Transactions on Recommender Systems最新文献

Self-Supervised Bot Play for Transcript-Free Conversational Critiquing with Rationales 无誊本对话评论的自监督机器人游戏与理由

ACM Transactions on Recommender Systems Pub Date : 2024-05-21 DOI: 10.1145/3665502

Shuyang Li, Bodhisattwa Prasad Majumder, Julian McAuley

{"title":"Self-Supervised Bot Play for Transcript-Free Conversational Critiquing with Rationales","authors":"Shuyang Li, Bodhisattwa Prasad Majumder, Julian McAuley","doi":"10.1145/3665502","DOIUrl":"https://doi.org/10.1145/3665502","url":null,"abstract":"\u0000 Conversational critiquing in recommender systems offers a way for users to engage in multi-turn conversations to find items they enjoy. For users to trust an agent and give effective feedback, the recommender system must be able to\u0000 explain\u0000 its suggestions and rationales. We develop a two-part framework for training multi-turn conversational critiquing in recommender systems that provide recommendation rationales that users can effectively interact with to receive better recommendations. First, we train a recommender system to jointly suggest items and explain its reasoning via subjective rationales. We then fine-tune this model to incorporate iterative user feedback via self-supervised bot-play. Experiments on three real-world datasets demonstrate that our system can be applied to different recommendation models across diverse domains to achieve state-of-the-art performance in multi-turn recommendation. Human studies show that systems trained with our framework provide more useful, helpful, and knowledgeable suggestions in warm- and cold-start settings. Our framework allows us to use only product reviews during training, avoiding the need for expensive dialog transcript datasets that limit the applicability of previous conversational recommender agents.\u0000","PeriodicalId":474833,"journal":{"name":"ACM Transactions on Recommender Systems","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Perception with Graph Attention Network for Recommendation 利用图形注意力网络进行社交感知推荐

ACM Transactions on Recommender Systems Pub Date : 2024-05-21 DOI: 10.1145/3665503

Jielin Jiang, Pengcheng Guo, Xiaolong Xu, Jintao Wu, Yan Cui

{"title":"Social Perception with Graph Attention Network for Recommendation","authors":"Jielin Jiang, Pengcheng Guo, Xiaolong Xu, Jintao Wu, Yan Cui","doi":"10.1145/3665503","DOIUrl":"https://doi.org/10.1145/3665503","url":null,"abstract":"\u0000 Recommendation systems are designed to uncover users’ potential preferences and make recommendations. However, they often face challenges such as data sparsity and the cold start problem. Although the introduction of knowledge graphs has partially addressed the issue of data sparsity, the challenge of cold start has not been effectively resolved. In this paper, a novel approach called\u0000 Social Perception with Graph Attention Network (SPGAT)\u0000 for Recommendation is proposed. In SPGAT, we aim to leverage social perception to solve the cold start effectively for more accurate recommendations. The approach utilizes a multi-layer graph attention network to aggregate user preference features from collaborative knowledge graphs and social perception graphs. By analyzing the social network of a new user, associated friend users can be identified. The interaction data of these friend users is then provided as side information to recommend to the new user. To handle one-to-many and many-to-many relations, we introduce the TransD graph embedding model, which maps different types of relations and entities to different spaces. To optimize the proposed SPGAT, self-adversarial negative sampling is utilized to implement entity and relation embedding and generate negative samples. Experimental results demonstrate that SPGAT has achieved superior performance compared to several advanced methods.\u0000","PeriodicalId":474833,"journal":{"name":"ACM Transactions on Recommender Systems","volume":"28 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Where are the Values? A Systematic Literature Review on News Recommender Systems 价值何在？关于新闻推荐系统的系统性文献综述

ACM Transactions on Recommender Systems Pub Date : 2024-03-28 DOI: 10.1145/3654805

Christine Bauer, Chandni Bagchi, Olusanmi A. Hundogan, Karin van Es

{"title":"Where are the Values? A Systematic Literature Review on News Recommender Systems","authors":"Christine Bauer, Chandni Bagchi, Olusanmi A. Hundogan, Karin van Es","doi":"10.1145/3654805","DOIUrl":"https://doi.org/10.1145/3654805","url":null,"abstract":"In the recommender systems field, it is increasingly recognized that focusing on accuracy measures is limiting and misguided. Unsurprisingly, in recent years, the field has witnessed more interest in the research of values “beyond accuracy.” This trend is particularly pronounced in the news domain where recommender systems perform parts of the editorial function, required to uphold journalistic values of news organizations. In the literature, various values and approaches have been proposed and evaluated. This paper reviews the current state of the proposed news recommender systems (NRS). We perform a systematic literature review, analyzing 183 papers. The primary aim is to study the development, scope, and focus of value-aware NRS over time. In contrast to previous surveys, we are particularly interested in identifying the range of values discussed and evaluated in the context of NRS, and embrace an interdisciplinary view. We identified a total of 40 values, categorized into five value groups. Most research on value-aware NRS has taken an algorithmic approach, whereas conceptual discussions are comparably scarce. Often, algorithms are evaluated by accuracy-based metrics, but the values are not evaluated with respective measures. Overall, our work identifies research gaps concerning values that have not received much attention. Values need to be targeted on a more fine-grained and specific level.","PeriodicalId":474833,"journal":{"name":"ACM Transactions on Recommender Systems","volume":"91 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems 从推荐系统的异构模型中进行无偏、有效和高效的提炼

ACM Transactions on Recommender Systems Pub Date : 2024-02-23 DOI: 10.1145/3649443

SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu

{"title":"Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems","authors":"SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu","doi":"10.1145/3649443","DOIUrl":"https://doi.org/10.1145/3649443","url":null,"abstract":"In recent years, recommender systems have achieved remarkable performance by using ensembles of heterogeneous models. However, this approach is costly due to the resources and inference latency proportional to the number of models, creating a bottleneck for production. Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), reducing inference costs while maintaining high accuracy. We find that the efficacy of distillation decreases when transferring knowledge from heterogeneous teachers. To address this, we propose a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from teachers’ trajectories. HetComp uses dynamic knowledge construction to provide progressively difficult ranking knowledge and adaptive knowledge transfer to gradually transfer finer-grained ranking information. Although HetComp improves accuracy, it exacerbates popularity bias, resulting in a high popularity lift. To mitigate this issue, we introduce two strategies that leverage models’ disagreement knowledge (i.e., dissensus) for heterogeneous comparison. Our experiments demonstrate that HetComp significantly enhances distillation quality and the student model’s generalization capabilities. Furthermore, we provide extensive experimental results supporting the effectiveness of our dissensus-based debiasing techniques in mitigating the popularity lift caused by HetComp.","PeriodicalId":474833,"journal":{"name":"ACM Transactions on Recommender Systems","volume":"30 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ranking the causal impact of recommendations under collider bias in k -spots recommender systems K 点推荐系统中对撞机偏差下的推荐因果影响排序

ACM Transactions on Recommender Systems Pub Date : 2024-01-31 DOI: 10.1145/3643139

Aleix Ruiz de Villa, Gabriele Sottocornola, Ludovik Çoba, Federico Lucchesi, Bartłomiej Skorulski

{"title":"Ranking the causal impact of recommendations under collider bias in\u0000 k\u0000 -spots recommender systems","authors":"Aleix Ruiz de Villa, Gabriele Sottocornola, Ludovik Çoba, Federico Lucchesi, Bartłomiej Skorulski","doi":"10.1145/3643139","DOIUrl":"https://doi.org/10.1145/3643139","url":null,"abstract":"\u0000 The first objective of recommender systems is to provide personalized recommendations for each user. However, personalization may not be its only use. Past recommendations can be further analyzed to gain global insights into users’ behavior with respect to recommended items. Such insights can help to answer design-related questions such as which items’ recommendations are the most impactful in terms of users’ utility, which type of recommendations are the most followed ones, which items could be dropped from the catalog, or which recommendations are under-performing compared to what one would expect. In order to answer those questions, we need to rank item recommendations’ performances in terms of their causal impact on some user-related outcome measures. Unfortunately, in previous work leveraging causal inference for recommendation systems, the attention is fully focused on correcting confounding bias and not on the collider bias. This bias is particularly relevant in the recommender context, where multiple items are simultaneously recommended. Indeed, when there is a fixed number of available spots (i.e.,\u0000 k\u0000 -spots) and recommendations need to be provided at each session, we argue that it is not possible to estimate the causal impacts of recommendations but only the differences between them. Therefore, in this paper, we provide an unbiased estimator of the differences in the impacts of items’ recommendations, that work for any outcome of interest, and any type of recommender system as long as it has some degree of randomization. We apply our results both in a simulated environment and in a real-world offline environment leveraging logged data for recommended items in a digital healthcare app.\u0000","PeriodicalId":474833,"journal":{"name":"ACM Transactions on Recommender Systems","volume":"22 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140478053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ACM Transactions on Recommender Systems Pub Date : 2023-12-11 DOI: 10.1145/3637066

Savvina Daniil, Mirjam Cuper, Cynthia C. S. Liem, Jacco Van Ossenbruggen, L. Hollink

引用次数: 0

RADio* – An Introduction to Measuring Normative Diversity in News Recommendations RADio* - 衡量新闻推荐中规范多样性的简介

ACM Transactions on Recommender Systems Pub Date : 2023-12-07 DOI: 10.1145/3636465

Sanne Vrijenhoek, G. Bénédict, Mateo Gutierrez Granada, Daan Odijk

引用次数: 0

Building Human Values into Recommender Systems: An Interdisciplinary Synthesis 在推荐系统中构建人的价值:跨学科综合

ACM Transactions on Recommender Systems Pub Date : 2023-11-13 DOI: 10.1145/3632297

Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Chloe Bakalar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Zhang, Mckane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan

{"title":"Building Human Values into Recommender Systems: An Interdisciplinary Synthesis","authors":"Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Chloe Bakalar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Zhang, Mckane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan","doi":"10.1145/3632297","DOIUrl":"https://doi.org/10.1145/3632297","url":null,"abstract":"Recommender systems are the algorithms which select, filter, and personalize content across many of the world's largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making.","PeriodicalId":474833,"journal":{"name":"ACM Transactions on Recommender Systems","volume":"63 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136347132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study 推荐系统中单项公平性评价方法的批判性研究

ACM Transactions on Recommender Systems Pub Date : 2023-11-09 DOI: 10.1145/3631943

Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Christina Lioma

{"title":"Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study","authors":"Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Christina Lioma","doi":"10.1145/3631943","DOIUrl":"https://doi.org/10.1145/3631943","url":null,"abstract":"Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we examine existing evaluation measures of fairness in recommender systems. Specifically, we focus solely on exposure-based fairness measures of individual items that aim to quantify the disparity in how individual items are recommended to users, separate from item relevance to users. We gather all such measures and we critically analyse their theoretical properties. We identify a series of limitations in each of them, which collectively may render the affected measures hard or impossible to interpret, to compute, or to use for comparing recommendations. We resolve these limitations by redefining or correcting the affected measures, or we argue why certain limitations cannot be resolved. We further perform a comprehensive empirical analysis of both the original and our corrected versions of these fairness measures, using real-world and synthetic datasets. Our analysis provides novel insights into the relationship between measures based on different fairness concepts, and different levels of measure sensitivity and strictness. We conclude with practical suggestions of which fairness measures should be used and when. Our code is publicly available. To our knowledge, this is the first critical comparison of individual item fairness measures in recommender systems.","PeriodicalId":474833,"journal":{"name":"ACM Transactions on Recommender Systems","volume":" 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135286141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CRS-Que : A User-Centric Evaluation Framework for Conversational Recommender Systems CRS-Que:会话推荐系统的以用户为中心的评估框架

ACM Transactions on Recommender Systems Pub Date : 2023-11-02 DOI: 10.1145/3631534

Yucheng Jin, Li Chen, Wanling Cai, Xianglin Zhao

{"title":"<i>CRS-Que</i> : A User-Centric Evaluation Framework for Conversational Recommender Systems","authors":"Yucheng Jin, Li Chen, Wanling Cai, Xianglin Zhao","doi":"10.1145/3631534","DOIUrl":"https://doi.org/10.1145/3631534","url":null,"abstract":"An increasing number of recommendation systems try to enhance the overall user experience by incorporating conversational interaction. However, evaluating conversational recommender systems (CRSs) from the user’s perspective remains elusive. The GUI-based system evaluation criteria may be inadequate for their conversational counterparts. This paper presents our proposed unifying framework, CRS-Que , to evaluate the user experience of CRSs. This new evaluation framework is developed based on ResQue , a popular user-centric evaluation framework for recommender systems. Additionally, it includes user experience metrics of conversation (e.g., understanding, response quality, humanness) under two dimensions of ResQue (i.e., Perceived Qualities and User Beliefs). Following the psychometric modeling method, we validate our framework by evaluating two conversational recommender systems in different scenarios: music exploration and mobile phone purchase . The results of the two studies support the validity and reliability of the constructs in our framework and reveal how conversation constructs and recommendation constructs interact and influence the overall user experience of the CRS. We believe this framework could help researchers conduct standardized user-centric research for conversational recommender systems and provide practitioners with insights into designing and evaluating a CRS from users’ perspectives.","PeriodicalId":474833,"journal":{"name":"ACM Transactions on Recommender Systems","volume":"14 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135875760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0