@Preamble{
"\hyphenation{ }"
}
@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",
}
