ACM Transactions on Recommender Systems CFP: RecSys 4 Good

[Marko Tkalcic]

CALL FOR PAPERS: ACM Transactions on Recommender Systems
Special Issue on Recommender Systems for Good
Submission deadline: 1. September 2024
https://dl.acm.org/pb-assets/static_journal_pages/tors/pdf/TORS_SI-Recommender-Systems-for-Good-1715281856967.pdf

Guest Editors:
- Marko Tkalčič, 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
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 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: September 1, 2024
- First-round review decisions: December 1, 2024
- Deadline for revision submissions: February 1, 2025
- Notification of final decisions: April 1, 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 rs4...@acm.org.

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Dr. Marko Tkalcic
http://markotkalcic.com
Twitter: https://twitter.com/#!/RecSysMare
Linkedin: http://www.linkedin.com/in/markotkalcic
Google Scholar: http://scholar.google.com/citations?user=JQ2puysAAAAJ
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