%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.03", %%% date = "30 April 2024", %%% time = "10:31:29 MST", %%% filename = "tors.bib", %%% address = "University of Utah %%% Department of Mathematics, 110 LCB %%% 155 S 1400 E RM 233 %%% Salt Lake City, UT 84112-0090 %%% USA", %%% telephone = "+1 801 581 5254", %%% FAX = "+1 801 581 4148", %%% URL = "https://www.math.utah.edu/~beebe", %%% checksum = "40084 960 4122 40506", %%% email = "beebe at math.utah.edu, beebe at acm.org, %%% beebe at computer.org (Internet)", %%% codetable = "ISO/ASCII", %%% keywords = "ACM Transactions on Recommender Systems; %%% bibliography; BibTeX ", %%% license = "public domain", %%% supported = "yes", %%% docstring = "This is a COMPLETE bibliography of the %%% journal ACM Transactions on Recommender %%% Systems (CODEN none, ISSN 2770-6699 %%% (electronic)). Publication began with volume %%% 1, number 1, in March 2023. %%% %%% The journal has World Wide Web sites at %%% %%% https://dl.acm.org/journal/tors %%% https://dl.acm.org/loi/tors %%% https://dl.acm.org/toc/tors/current %%% %%% At version 1.03, the year coverage looked %%% like this: %%% %%% 2023 ( 20) 2024 ( 11) %%% %%% Article: 31 %%% %%% Total entries: 31 %%% %%% The checksum field above contains a CRC-16 %%% checksum as the first value, followed by the %%% equivalent of the standard UNIX wc (word %%% count) utility output of lines, words, and %%% characters. This is produced by Robert %%% Solovay's checksum utility.", %%% } %%% ==================================================================== @Preamble{ "\hyphenation{ }" # "\ifx \undefined \booktitle \def \booktitle #1{{{\em #1}}} \fi" } %%% ==================================================================== %%% Acknowledgement abbreviations: @String{ack-nhfb = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|https://www.math.utah.edu/~beebe/|"} %%% ==================================================================== %%% Journal abbreviations: @String{j-TORS = "ACM Transactions on Recommender Systems (TORS)"} %%% ==================================================================== %%% Bibliography entries: @Article{Chen:2023:ATR, author = "Li Chen and Dietmar Jannach", title = "{{\booktitle{ACM Transactions on Recommender Systems}}}: Inaugural Issue Editorial", journal = j-TORS, volume = "1", number = "1", pages = "1:1--1:??", month = mar, year = "2023", DOI = "https://doi.org/10.1145/3569454", ISSN = "2770-6699", bibdate = "Wed Apr 5 15:40:16 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3569454", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Silva:2023:UCS, author = "Nicollas Silva and Thiago Silva and Heitor Werneck and Leonardo Rocha and Adriano Pereira", title = "User Cold-start Problem in Multi-armed Bandits: When the First Recommendations Guide the User's Experience", journal = j-TORS, volume = "1", number = "1", pages = "2:1--2:??", month = mar, year = "2023", DOI = "https://doi.org/10.1145/3554819", ISSN = "2770-6699", bibdate = "Wed Apr 5 15:40:16 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3554819", abstract = "Nowadays, Recommender Systems have played a crucial role in several entertainment scenarios by making personalised recommendations and guiding the \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Gao:2023:SGN, author = "Chen Gao and Yu Zheng and Nian Li and Yinfeng Li and Yingrong Qin and Jinghua Piao and Yuhan Quan and Jianxin Chang and Depeng Jin and Xiangnan He and Yong Li", title = "A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions", journal = j-TORS, volume = "1", number = "1", pages = "3:1--3:??", month = mar, year = "2023", DOI = "https://doi.org/10.1145/3568022", ISSN = "2770-6699", bibdate = "Wed Apr 5 15:40:16 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3568022", abstract = "Recommender system is one of the most important information services on today's Internet. Recently, graph neural networks have become the new \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Jeunen:2023:PDM, author = "Olivier Jeunen and Bart Goethals", title = "Pessimistic Decision-Making for Recommender Systems", journal = j-TORS, volume = "1", number = "1", pages = "4:1--4:??", month = mar, year = "2023", DOI = "https://doi.org/10.1145/3568029", ISSN = "2770-6699", bibdate = "Wed Apr 5 15:40:16 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3568029", abstract = "Modern recommender systems are often modelled under the sequential decision-making paradigm, where the system decides which recommendations to show \ldots{}", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Liu:2023:DRL, author = "Dugang Liu and Pengxiang Cheng and Hong Zhu and Zhenhua Dong and Xiuqiang He and Weike Pan and Zhong Ming", title = "Debiased Representation Learning in Recommendation via Information Bottleneck", journal = j-TORS, volume = "1", number = "1", pages = "5:1--5:??", month = mar, year = "2023", DOI = "https://doi.org/10.1145/3568030", ISSN = "2770-6699", bibdate = "Wed Apr 5 15:40:16 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3568030", abstract = "How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this article, we first describe the generation \ldots{}", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Srba:2023:AYR, author = "Ivan Srba and Robert Moro and Matus Tomlein and Branislav Pecher and Jakub Simko and Elena Stefancova and Michal Kompan and Andrea Hrckova and Juraj Podrouzek and Adrian Gavornik and Maria Bielikova", title = "Auditing {YouTube}'s Recommendation Algorithm for Misinformation Filter Bubbles", journal = j-TORS, volume = "1", number = "1", pages = "6:1--6:??", month = mar, year = "2023", DOI = "https://doi.org/10.1145/3568392", ISSN = "2770-6699", bibdate = "Wed Apr 5 15:40:16 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3568392", abstract = "In this article, we present results of an auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter \ldots{}", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Coscrato:2023:EEU, author = "Victor Coscrato and Derek Bridge", title = "Estimating and Evaluating the Uncertainty of Rating Predictions and Top-$n$ Recommendations in Recommender Systems", journal = j-TORS, volume = "1", number = "2", pages = "7:1--7:??", month = jun, year = "2023", DOI = "https://doi.org/10.1145/3584021", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3584021", abstract = "Uncertainty is a characteristic of every data-driven application, including recommender systems. The quantification of uncertainty can be key to increasing user trust in recommendations or choosing which recommendations should be accompanied by an \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "7", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Li:2023:EWG, author = "Xueqi Li and Guoqing Xiao and Yuedan Chen and Zhuo Tang and Wenjun Jiang and Kenli Li", title = "An Explicitly Weighted {GCN} Aggregator based on Temporal and Popularity Features for Recommendation", journal = j-TORS, volume = "1", number = "2", pages = "8:1--8:??", month = jun, year = "2023", DOI = "https://doi.org/10.1145/3587272", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3587272", abstract = "Graph convolutional network (GCN) has been extensively applied to recommender systems (RS) and achieved significant performance improvements through iteratively aggregating high-order neighbors to model the relevance between users and items as well as \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "8", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Zhou:2023:SSF, author = "Xin Zhou and Aixin Sun and Yong Liu and Jie Zhang and Chunyan Miao", title = "{SelfCF}: a Simple Framework for Self-supervised Collaborative Filtering", journal = j-TORS, volume = "1", number = "2", pages = "9:1--9:??", month = jun, year = "2023", DOI = "https://doi.org/10.1145/3591469", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3591469", abstract = "Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. That is, observed user-item \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "9", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Li:2023:WWP, author = "Ming Li and Mozhdeh Ariannezhad and Andrew Yates and Maarten {De Rijke}", title = "Who Will Purchase This Item Next? {Reverse} Next Period Recommendation in Grocery Shopping", journal = j-TORS, volume = "1", number = "2", pages = "10:1--10:??", month = jun, year = "2023", DOI = "https://doi.org/10.1145/3595384", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3595384", abstract = "Recommender systems have become an essential instrument to connect people to the items that they need. Online grocery shopping is one scenario where this is very clear. So-called user-centered recommendations take a user as input and suggest items based \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "10", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Cavenaghi:2023:SSR, author = "Emanuele Cavenaghi and Gabriele Sottocornola and Fabio Stella and Markus Zanker", title = "A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation Systems", journal = j-TORS, volume = "1", number = "3", pages = "11:1--11:??", month = sep, year = "2023", DOI = "https://doi.org/10.1145/3596519", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3596519", abstract = "Reproducibility is a main principle in science and fundamental to ensure scientific progress. However, many recent works point out that there are widespread deficiencies for this aspect in the AI field, making the reproducibility of results impractical or \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "11", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Tomlinson:2023:TTM, author = "Kiran Tomlinson and Mengting Wan and Cao Lu and Brent Hecht and Jaime Teevan and Longqi Yang", title = "Targeted Training for Multi-organization Recommendation", journal = j-TORS, volume = "1", number = "3", pages = "12:1--12:??", month = sep, year = "2023", DOI = "https://doi.org/10.1145/3603508", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3603508", abstract = "Making recommendations for users in diverse organizations ( orgs ) is a challenging task for workplace social platforms such as Microsoft Teams and Slack. The current industry-standard model training approaches either use data from all organizations to \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "12", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Benedict:2023:ISM, author = "Gabriel B{\'e}n{\'e}dict and Daan Odijk and Maarten de Rijke", title = "Intent-Satisfaction Modeling: From Music to Video Streaming", journal = j-TORS, volume = "1", number = "3", pages = "13:1--13:??", month = sep, year = "2023", DOI = "https://doi.org/10.1145/3606375", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3606375", abstract = "Logged behavioral data is a common resource for enhancing the user experience on streaming platforms. In music streaming, Mehrotra et al. have shown how complementing behavioral data with user intent can help predict and explain user satisfaction. Do \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "13", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Rendle:2023:RUI, author = "Steffen Rendle and Li Zhang", title = "On Reducing User Interaction Data for Personalization", journal = j-TORS, volume = "1", number = "3", pages = "14:1--14:??", month = sep, year = "2023", DOI = "https://doi.org/10.1145/3600097", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3600097", abstract = "Most recommender systems rely on user interaction data for personalization. Usually, the recommendation quality improves with more data. In this work, we study the quality implications when limiting user interaction data for personalization purposes. We \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "14", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Nguyen:2023:TCP, author = "Tung Nguyen and Jeffrey Uhlmann", title = "Tensor Completion with Provable Consistency and Fairness Guarantees for Recommender Systems", journal = j-TORS, volume = "1", number = "3", pages = "15:1--15:??", month = sep, year = "2023", DOI = "https://doi.org/10.1145/3604649", ISSN = "2770-6699", bibdate = "Fri Aug 25 11:02:37 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3604649", abstract = "We introduce a new consistency-based approach for defining and solving nonnegative/positive matrix and tensor completion problems. The novelty of the framework is that instead of artificially making the problem well-posed in the form of an application-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "15", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Zhang:2023:GLA, author = "Yiming Zhang and Lingfei Wu and Qi Shen and Yitong Pang and Zhihua Wei and Fangli Xu and Ethan Chang and Bo Long", title = "Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation", journal = j-TORS, volume = "1", number = "4", pages = "16:1--16:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3610407", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3610407", abstract = "Social recommendation based on social network has achieved great success in improving the performance of the recommendation system. Since social network (user-user relations) and user-item interactions are both naturally represented as graph-structured \ldots{}", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Xu:2023:DCC, author = "Shuyuan Xu and Juntao Tan and Shelby Heinecke and Vena Jia Li and Yongfeng Zhang", title = "Deconfounded Causal Collaborative Filtering", journal = j-TORS, volume = "1", number = "4", pages = "17:1--17:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3606035", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3606035", abstract = "Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually design each \ldots{}", acknowledgement = ack-nhfb, articleno = "17", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Starke:2023:EUE, author = "Alain D. Starke and Edis Asotic and Christoph Trattner and Ellen J. {Van Loo}", title = "Examining the User Evaluation of Multi-List Recommender Interfaces in the Context of Healthy Recipe Choices", journal = j-TORS, volume = "1", number = "4", pages = "18:1--18:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3581930", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3581930", abstract = "Multi-list recommender systems have become widespread in entertainment and e-commerce applications. Yet, extensive user evaluation research is missing. Since most content is optimized toward a user's current preferences, this may be problematic in \ldots{}", acknowledgement = ack-nhfb, articleno = "18", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Ferrara:2023:KER, author = "Antonio Ferrara and Vito Walter Anelli and Alberto Carlo Maria Mancino and Tommaso {Di Noia} and Eugenio {Di Sciascio}", title = "{KGFlex}: Efficient Recommendation with Sparse Feature Factorization and Knowledge Graphs", journal = j-TORS, volume = "1", number = "4", pages = "19:1--19:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3588901", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3588901", abstract = "Collaborative filtering models have undoubtedly dominated the scene of recommender systems in recent years. However, due to the little use of content information, they narrowly focus on accuracy, disregarding a higher degree of personalization. Meanwhile, \ldots{}", acknowledgement = ack-nhfb, articleno = "19", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Lim:2023:LHS, author = "Nicholas Lim and Bryan Hooi and See-Kiong Ng and Yong Liang Goh and Renrong Weng and Rui Tan", title = "Learning Hierarchical Spatial Tasks with Visiting Relations for Next {POI} Recommendation", journal = j-TORS, volume = "1", number = "4", pages = "20:1--20:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3610584", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3610584", abstract = "Sparsity is an established problem for the next Point-of-Interest (POI) recommendation task, where it hinders effective learning of user preferences from the User-POI matrix. However, learning multiple hierarchically related spatial tasks, and visiting \ldots{}", acknowledgement = ack-nhfb, articleno = "20", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Bauer:2024:ISI, author = "Christine Bauer and Alan Said and Eva Zangerle", title = "Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation", journal = j-TORS, volume = "2", number = "1", pages = "1:1--1:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3648398", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3648398", abstract = "Evaluation plays a vital role in recommender systems-in research and practice-whether for confirming algorithmic concepts or assessing the operational validity of designs and applications. It may span the evaluation of early ideas and approaches up to \ldots{}", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Jin:2024:CQU, author = "Yucheng Jin and Li Chen and Wanling Cai and Xianglin Zhao", title = "{CRS-Que}: a User-centric Evaluation Framework for Conversational Recommender Systems", journal = j-TORS, volume = "2", number = "1", pages = "2:1--2:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3631534", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3631534", 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 \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Porcaro:2024:AIM, author = "Lorenzo Porcaro and Emilia G{\'o}mez and Carlos Castillo", title = "Assessing the Impact of Music Recommendation Diversity on Listeners: a Longitudinal Study", journal = j-TORS, volume = "2", number = "1", pages = "3:1--3:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3608487", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3608487", abstract = "We present the results of a 12-week longitudinal user study wherein the participants, 110 subjects from Southern Europe, received on a daily basis Electronic Music (EM) diversified recommendations. By analyzing their explicit and implicit feedback, we \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Michiels:2024:FTT, author = "Lien Michiels and Robin Verachtert and Andres Ferraro and Kim Falk and Bart Goethals", title = "A Framework and Toolkit for Testing the Correctness of Recommendation Algorithms", journal = j-TORS, volume = "2", number = "1", pages = "4:1--4:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3591109", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3591109", abstract = "Evaluating recommender systems adequately and thoroughly is an important task. Significant efforts are dedicated to proposing metrics, methods, and protocols for doing so. However, there has been little discussion in the recommender systems' literature on \ldots{}", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Daniil:2024:RPB, author = "Savvina Daniil and Mirjam Cuper and Cynthia C. S. Liem and Jacco van Ossenbruggen and Laura Hollink", title = "Reproducing Popularity Bias in Recommendation: The Effect of Evaluation Strategies", journal = j-TORS, volume = "2", number = "1", pages = "5:1--5:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3637066", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3637066", abstract = "The extent to which popularity bias is propagated by media recommender systems is a current topic within the community, as is the uneven propagation among users with varying interests for niche items. Recent work focused on exactly this topic, with movies \ldots{}", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Ekstrand:2024:DIR, author = "Michael D. Ekstrand and Ben Carterette and Fernando Diaz", title = "Distributionally-Informed Recommender System Evaluation", journal = j-TORS, volume = "2", number = "1", pages = "6:1--6:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3613455", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3613455", abstract = "Current practice for evaluating recommender systems typically focuses on point estimates of user-oriented effectiveness metrics or business metrics, sometimes combined with additional metrics for considerations such as diversity and novelty. In this \ldots{}", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Li:2024:ISE, author = "Dong Li and Ruoming Jin and Zhenming Liu and Bin Ren and Jing Gao and Zhi Liu", title = "On Item-Sampling Evaluation for Recommender System", journal = j-TORS, volume = "2", number = "1", pages = "7:1--7:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3629171", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3629171", abstract = "Personalized recommender systems play a crucial role in modern society, especially in e-commerce, news, and ads areas. Correctly evaluating and comparing candidate recommendation models is as essential as constructing ones. The common offline evaluation \ldots{}", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{AlJurdi:2024:GVR, author = "Wissam {Al Jurdi} and Jacques Bou Abdo and Jacques Demerjian and Abdallah Makhoul", title = "Group Validation in Recommender Systems: Framework for Multi-layer Performance Evaluation", journal = j-TORS, volume = "2", number = "1", pages = "8:1--8:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3640820", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3640820", abstract = "Evaluation of recommendation systems continues evolving, especially in recent years. There have been several attempts to standardize the assessment processes and propose replacement metrics better oriented toward measuring effective personalization. \ldots{}", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Rahdari:2024:TSB, author = "Behnam Rahdari and Peter Brusilovsky and Branislav Kveton", title = "Towards Simulation-Based Evaluation of Recommender Systems with Carousel Interfaces", journal = j-TORS, volume = "2", number = "1", pages = "9:1--9:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643709", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3643709", abstract = "Offline data-driven evaluation is considered a low-cost and more accessible alternative to the online empirical method of assessing the quality of recommender systems. Despite their popularity and effectiveness, most data-driven approaches are unsuitable \ldots{}", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Ferraro:2024:MCR, author = "Andres Ferraro and Gustavo Ferreira and Fernando Diaz and Georgina Born", title = "Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural Citizenship", journal = j-TORS, volume = "2", number = "1", pages = "10:1--10:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643138", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3643138", abstract = "Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience. While the majority of academic and industrial research on recommender systems optimizes for \ldots{}", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", } @Article{Bauer:2024:ELR, author = "Christine Bauer and Eva Zangerle and Alan Said", title = "Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives", journal = j-TORS, volume = "2", number = "1", pages = "11:1--11:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3629170", ISSN = "2770-6699", ISSN-L = "2770-6699", bibdate = "Tue Apr 30 10:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tors.bib", URL = "https://dl.acm.org/doi/10.1145/3629170", abstract = "Recommender systems research and practice are fast-developing topics with growing adoption in a wide variety of information access scenarios. In this article, we present an overview of research specifically focused on the evaluation of recommender \ldots{}", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Recommender Systems (TORS)", journal-URL = "https://dl.acm.org/loi/tors", }