ACM TORS Call for Papers: Special Issue on Recommender Systems for Good
The ACM Transactions on Recommender Systems has announced a special issue on “Recommender Systems for Good,” led by guest editors Marko Tkalčič, Noemi Mauro, Alan Said, Nava Tintarev, and Antonela Tommasel. This issue emphasizes the broader impact of recommender systems beyond traditional business metrics like sales and customer retention. The focus is on leveraging these systems for societal benefits, such as promoting healthier behaviors, aiding low-income families in education, encouraging energy-saving habits, supporting fair micro-lending, and improving the quality of news consumption. The editors call for contributions that address these societal challenges, advocating for a multidisciplinary approach that incorporates insights from computer science, ethics, sociology, psychology, law, and economics.
The special issue seeks to establish methodologies and evaluation criteria to measure the effectiveness and ethical implications of recommender systems aimed at societal good. Researchers are encouraged to consider conflicting values and determine how to prioritize them. The goal is to foster an interdisciplinary dialogue and collaboration to ensure that the developed systems are grounded in real-world needs and values. The initiative aligns with broader movements like “AI for Good” and aims to contribute to achieving the United Nations Sustainable Development Goals and the UNESCO recommendation on the Ethics of Artificial Intelligence.
The call for papers invites technical research papers, surveys, and reflective pieces that address the societal impact of recommender systems. Submissions should be prepared according to the ACM TORS guidelines and submitted via Manuscript Central by September 1, 2024, with first-round review decisions by December 1, 2024, and final decisions by April 1, 2025. The special issue also welcomes peer-reviewed journal versions of top conference papers, provided they offer at least 30% new content. For further information, the guest editors can be contacted at rs4good@acm.org.
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About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).