@Preamble{
"\hyphenation{ }" #
"\ifx \undefined \booktitle \def \booktitle #1{{{\em #1}}} \fi"
}
@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/|"}
@String{j-TORS = "ACM Transactions on Recommender Systems
(TORS)"}
@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",
}
