ACM Transactions on Recommender Systems CFP: RecSys 4 Good

CALL FOR PAPERS: ACM Transactions on Recommender Systems
Special Issue on Recommender Systems for Good
Submission deadline: 24. December 2024

Guest Editors:
- Marko Tkal=C4=8Di=C4=8D, University of Primorska, Slovenia
- Noemi Mauro, University of Turin, Italy
- Alan Said, University of Gothenburg, Sweden
- Nava Tintarev, University of Maastricht, Netherlands
- Antonela Tommasel, ISISTAN, CONICET-UNCPBA, Argentina

Recommender systems are among the most widely used applications of machin=
e learning. Since they are so widely used, it is important that we, as pr=
actitioners and researchers, think about the impact these systems may hav=
e on users, society, and other stakeholders. In practice, the focus is of=
ten 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 bu=
siness objectives of individual corporations.

However, other values, bound more to societal good, could be considered i=
n the development and goals of a recommender system. In fact, recommender=
 systems have already been explored to stimulate healthier eating behavio=
r and for improved health and well-being in general, to help low-income f=
amilies make school choices, to suggest successful learning paths for stu=
dents, to entice climate-protecting energy-saving behavior, to support fa=
ir micro-lending, or improve the information diets of news readers. Resea=
rch in these areas is however limited in numbers, compared to the many pa=
pers that are published every year that propose new models for improved m=
ovie recommendations.

Moreover, concerning the methodology and evaluation perspective in this a=
rea, it is essential to find a clear methodology and criteria for evaluat=
ing the effectiveness and "goodness" of the proposed algorithms. This inc=
ludes acknowledging that different values may be conflicting, as well as =
resolving how and when (and by whom) certain values should be prioritized=
 over others such as in the NORMalize workshop (https://sites.google.com/=
view/normalizeworkshop).

Research on "Recommender Systems for Good" may benefit from an interdisci=
plinary approach, drawing on insights from fields such as computer scienc=
e, 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 u=
rgent 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 t=
he spirit of the "AI for Good" initiative (https://aiforgood.itu.int) to =
achieve the United Nations Sustainable Development Goals (2015) and the m=
ore 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 p=
overty 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 s=
olidarity, online conversational health, multi-stakeholder recommenders)
- Accountability in recommender systems, including addressing emerging re=
gulations, such as the DSA (Digital Service Act)
- Recommender systems for the public good (e.g., mental and physical heal=
th, welfare, digital literacy, stakeholder engagement, e-learning)
- Introspective studies on the current state of RSs concerning societal g=
ood
- Fairness-preserving and fairness-enhancing recommender systems, unbiase=
d 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 herita=
ge)
- Recommendations to support disadvantaged groups (e.g., elderly, minorit=
ies)
- Recommender systems for personal development and well-being (e.g., beha=
vioral 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 ac=
ceptance.

Submission Information:
The special issue welcomes technical research papers, survey papers, and =
opinion/reflective papers. Each paper should address one or more of the a=
bovementioned topics or be in other scopes of Recommender Systems for Goo=
d. The special issue will also consider peer-reviewed journal versions (a=
t least 30% new content) of top papers from related recommender system co=
nferences such as RecSys, SIGIR, KDD, CIKM, IUI, UMAP, CHI, WSDM, ACL, et=
c. Prospective authors may take advantage of submitting an early version =
of their work to the ACM RecSys RecSoGood Workshop https://recsogood.gith=
ub.io/recsogood24/. The new content must be in terms of intellectual cont=
ributions, 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 a=
t rs4good [at] acm [dot] org.

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