[Kdml] 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: 1. September 2024 https://dl.acm.org/pb-assets/static_journal_pages/tors/pdf/TORS_SI-Recommen… 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 rs4good(a)acm.org. -- ---------------------------------------------------------------------- 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 ----------------------------------------------------------------------