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 for Good
Submission Date: 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.