ACM Transactions on Recommender Systems (TORS) publishes high quality papers that address various aspects of recommender systems research, from algorithms to user experience, to questions of the impact and value of such systems on a quarterly basis.

The journal takes a holistic view on the field and calls for contributions from different subfields of computer science and information systems, such as machine learning, data mining, information retrieval, web-based systems, data science and big data, and human-computer interaction. Moreover, interdisciplinary research works are welcome as well. Such works may either be based on insights from related fields, e.g., marketing or psychology, or apply recommendation technology in novel application areas. 

Special Issue on Recommender Systems in Industry: Challenges and Solutions
截稿日期: 2024-12-01

Recommender systems are nowadays used across a variety of application settings, where they have shown to be
able to deliver substantial values both for service providers and consumers. Given the high practical relevance of
these systems, academic interest in this area has been constantly increasing over the years.
Historically, there have always been strong ties between industry and academia in this field. There are countless
examples where organizations deploy recommendation models that were the result of academic research. Also,
the usually large fraction of participants from the industry at the yearly ACM Conference on Recommender Systems
provides strong evidence of the intense exchange between scholars from academia, and researchers and
practitioners from industry.

Nonetheless, a certain gap between academic research and industrial needs seems to remain. Today, academic
research is to a large extent focusing on developing general-purpose, domain-independent recommendation
models, which are evaluated and benchmarked in data-based offline experiments. This development is in some
ways only natural, as it is a common goal of academic scholars to develop generalizable solutions that not only work
in one particular application setting. However, such an approach comes with the danger that academics rely on a
research operationalization that is overly generic and potentially abstracts too much from the particularities of real-
world problem settings.

With this special issue, we would like to shed light on recent progress and ongoing challenges of designing and
implementing recommender systems in practical environments. As a consequence, the contributions in this issue
should guide academic scholars toward a better understanding of important questions from the real world that still
need to be addressed through fundamental academic research or through future academia-industry collaborations.
In the end, we hope that the papers in this special issue will stimulate even more impactful academic research in
the field of recommender systems.

Topics:
We aim to cover a wide spectrum of topics related to the design and use of recommender systems in practice.
The topics of the special issue include (but are not limited to):
● Application and domain-specific challenges, solutions, and opportunities
● Case studies, best-practice reports, success and failure stories
● Collaboration reports between academia and industry
● Measuring business and consumer value, including multistakeholder considerations
● Addressing the offline and online evaluation gap
● Data engineering challenges relating, e.g., to data sparsity, data acquisition, data quality, data
integration, or information pipeline design
● Architectures for large-scale recommender systems and scalability
● Addressing recommendation biases, temporal and seasonal effects
● Use of Large Language Models in real-world settings
● Privacy, security, transparency and regulatory challenges
● Identification of bots / fake user profiles
● User interface design challenges
● Organizational challenges, e.g., cross-team collaborations, competing stakeholder goals

Important Dates
• Submission deadline: December 1, 2024
• First-round review decisions: March 1, 2025
• Deadline for revision submissions: May 1, 2025
• Notification of final decisions: July 1, 2025

Submission Information
While there is no technical page limit, we expect that most industry reports published in this special issue are about
10-20 pages in length in the ACM submission format, and thus shorter than typical technical journal contributions.
Correspondingly, we do not necessarily expect a detailed technical exposition or novel technical contributions in
industry reports. Instead, we expect that the papers in this special issue report on real-world challenges and
solutions, to further narrow the gap between academic research and industrial practice.
Significantly extended versions of previously published “Industry track” conference papers are welcome. Please
contact the editors when in doubt.
Submissions must be prepared according to the TORS submission guidelines
(https://dl.acm.org/journal/tors/author-guidelines) and must be submitted via Manuscript Central
(https://mc.manuscriptcentral.com/tors).
We advise authors to obtain company approval, if needed, in advance of submission.
For questions and further information, please contact the editors at tors-eics@acm.org.

Special Issue on User Interaction Design for Human-Centered Recommender Systems
截稿日期: 2024-12-15

Guest Editors:
• Yucheng Jin, Duke Kunshan University, China, yj232@duke.edu
• Wanling Cai, Trinity College Dublin & Lero, Ireland wanling.cai@tcd.ie
• Bart Knijnenburg, Clemson University, USA, bartk@clemson.edu
• Katrien Verbert, KU Leuven, Belgium, katrien.verbert@kuleuven.be

Human-centered recommender systems (HCRS) are designed with a focus on the human user, taking into account
human needs, values, behavior, and experiences throughout the design process of the system. The goal is to
create systems that are not just technically proficient at generating accurate recommendations but are also
engaging, transparent, trustworthy, and aligned with user preferences and societal norms. To achieve this goal,
the need for effective user interactions in recommender systems is more critical than ever.

Research in user interaction for recommender systems focuses on how users engage with recommender systems
and how the interaction with the systems can be improved to provide more relevant, personalized, and engaging
content. This research area is crucial because the effectiveness of a recommender system is not just about the
accuracy of the predictions but also about how users perceive, interact with, and respond to the
recommendations. For instance, effective UIs could support transparency and user control of recommender
systems. Aligning the vision of human-centered AI, HCRS should preserve human control in a way that ensures the
automation part of recommender systems could amplify human capabilities in decision-making.

Moreover, in recent years, the growing capabilities of large language models (LLM) have influenced not only the
substance of recommendations but also the way users can interact with them. The surge in natural language
processing capabilities has opened new avenues for how recommender systems are developed and interacted
with by the end-users, and the conversational user interface has been increasingly popular for various AI systems.
More research on novel UIs beyond the GUI could be the key to designing more transparent, explainable, and
interactive recommender systems.

Topics
In this special issue, contributors have the opportunity to research a variety of themes within the realm of user
interaction design for recommender systems (RS). We encourage submissions that push the boundaries of
traditional interaction design, exploring new methods to enhance user interaction and satisfaction. The main
themes and topics of the special issue include, but are not limited to:
• Understanding Users of RS
• User Interfaces and Visualizations for RS
• Conversational User Interfaces for RS
• User Interaction with LLM-based RS
• AR/VR Interface Design for RS
• User Interface for Explainability, Transparency, and Trust
• User Interface for User Control and Customization
• User-Adaptive Interaction and Personalization
• User Evaluation for the Interaction with RS
• Datasets for User Interaction Behavior in RS
• Platforms and Questionnaires for RS User Studies

Important Dates
• Submission deadline: December 15, 2024
• First-round review decisions: March 15, 2025
• Deadline for revision submissions: May 15, 2025
• Notification of final decisions: July 15, 2025
Submissions that are received before the first deadline will be directly sent out for review; papers will be
immediately published online after acceptance.

Submission Information:
The special edition is open to submissions across a variety of formats including technical research, design
research, user studies, surveys, and reflective or opinion pieces. Submissions should be relevant to one or more
topics within the scope of the special edition. We are particularly interested in contributions that explore
interaction design within recommender systems, leveraging cutting-edge technologies like generative AI and
augmented/virtual/mixed reality. Additionally, this edition is open to extended versions of published papers from
related recommender system conferences such as RecSys, SIGIR, KDD, CIKM, IUI, UMAP, CHI, WSDM, and WWW.
These submissions must include at least 30% new material, which may consist of novel intellectual insights,
experimental work, or research findings. If you are unsure about whether your work fit this special issue, please
do not hesitate to contact us.
Submissions must be prepared according to the TORS submission guidelines
(https://dl.acm.org/journal/tors/author-guidelines) and must be submitted via Manuscript Central
(https://mc.manuscriptcentral.com/tors).
For questions and further information, please contact the guest editors at uid4rec@acm.org.

Special Issue on Recommender Systems for Good
截稿日期: 2024-12-24

Guest Editors:
• Marko Tkalčič, University of Primorska, Slovenia, marko.tkalcic@gmail.com
• Noemi Mauro, University of Turin, Italy, noemi.mauro@unito.it
• Alan Said, University of Gothenburg, Sweden, alansaid@acm.org
• Nava Tintarev, University of Maastricht, Netherlands, n.tintarev@maastrichtuniversity.nl
• Antonela Tommasel, ISISTAN, CONICET-UNCPBA, Argentina, antonela.tommasel@isistan.unicen.edu.ar

Recommender systems are among the most widely used applications of machine learning. Since they are so widely
used, it is important that we, as practitioners and researchers, think about the impact these systems may have on
users, society, and other stakeholders. In practice, the focus is often on systems and values of improving key
performance indicators (KPIs), such as increased sales or customer retention. Recommendation technology is
currently underutilized to serve societal goals that go beyond the business objectives of individual corporations.
However, other values, bound more to societal good, could be considered in the development and goals of a
recommender system. In fact, recommender systems have already been explored to stimulate healthier eating
behavior and for improved health and well-being in general, to help low-income families to make school choices,
to suggest successful learning paths for students, to entice climate-protecting energy-saving behavior, to support
fair micro-lending, or improve the information diets of news readers. Research in these areas is however limited in
numbers, compared to the many papers that are published every year that propose new models for improved movie
recommendations.

Moreover, concerning the methodology and evaluation perspective in this area, it is essential to find a clear
methodology and criteria for evaluating the effectiveness and "goodness" of the proposed algorithms. This includes
acknowledging that different values may be conflicting, as well as resolving how and when (and by whom) certain
values should be prioritized over others.

Research on "Recommender Systems for Good" may benefit from an interdisciplinary approach, drawing on insights
from fields such as computer science, ethics, sociology, psychology, law, and economics. Collaborations with
stakeholders from diverse backgrounds can enrich the research and ensure that recommendations are grounded in
real-world needs and values.

This special issue aims to present state-of-the-art research works where recommender systems have a positive
societal impact and help us address urgent societal challenges. It will thereby serve as a call to action for more
research in these areas. Ultimately, through this special issue, we hope to establish a vision of "Recommender
Systems for Good', following the spirit of the "AI for Good" initiative (https://aiforgood.itu.int) to achieve the United
Nations Sustainable Development Goals (2015) and the more recent UNESCO recommendation on the Ethics of
Artificial Intelligence (2024) (https://www.unesco.org/en/artificial-intelligence/recommendation-ethics).

Topics:
We aim to collect the latest research on recommender systems for societal good. The topics of the special issues
include (but are not limited to):
● Recommender systems for safety, security, and privacy (e.g., reducing poverty and inequality)
● Recommender systems that protect the environment and ecosystems (e.g., lower energy
consumption, water and energy management)
● Recommender systems that give control of data back to the users (e.g., transparency of data, models,
and outputs)
● Recommender systems for the interconnected society (e.g., increase of solidarity, online
conversational health, multi-stakeholder recommenders)
● Accountability in recommender systems, including addressing emerging regulations, such as the DSA
(Digital Service Act)
● Recommender systems for the public good (e.g., mental and physical health, welfare, digital literacy,
stakeholder engagement, e-learning)
● Introspective studies on the current state of RSs concerning societal good
● Fairness-preserving and fairness-enhancing recommender systems, unbiased recommendations (e.g.
to preserve gender equality)
● Responsible recommendation (e.g., in social media and traditional news, avoiding filter bubbles and
echo chambers)
● Sustainability and Cultural recommendations (e.g., art, cultural heritage)
● Recommendations to support disadvantaged groups (e.g., elderly, minorities)
● Recommender systems for personal development and well-being (e.g., behavioral change, fitness, self-
actualization, personal growth)

Important Dates
• Submission deadline: December 24, 2024
• First-round review decisions: March 24, 2025
• Deadline for revision submissions: May 24, 2025
• Notification of final decisions: June 24, 2025
Submissions that are received before the first deadline will be directly sent out for review; papers will be
immediately published online after acceptance.

Submission Information
The special issue welcomes technical research papers, survey papers and opinion/reflective papers. Each paper
should address one or more of the abovementioned topics or be in other scopes of Recommender Systems for
Good. The special issue will also consider peer-reviewed journal versions (at least 30% new content) of top papers
from related recommender system conferences such as RecSys, SIGIR, KDD, CIKM, IUI, UMAP, CHI, WSDM, ACL, etc.
The new content must be in terms of intellectual contributions, technical experiments, and findings.
Submissions must be prepared according to the TORS submission guidelines
(https://dl.acm.org/journal/tors/author-guidelines) and must be submitted via Manuscript Central
(https://mc.manuscriptcentral.com/tors).
For questions and further information, please contact the guest editors at rs4good@acm.org.