New Directions for News Recommender Systems: ACM TORS Special Issue (CfP)
ACM Transactions on Recommender Systems (ACM TORS) is launching a Special Issue on “New Directions for News Recommender Systems”— and ACM TORS is inviting submissions from both academia and industry. The issue is guest edited by Joseph Konstan (University of Minnesota), Özlem Özgöbek (Norwegian University of Science and Technology) and Edward Malthouse (Northwestern University).
News recommendation is its own beast: rapid content turnover, perpetual cold start, rich multimodal content, weak/implicit feedback (headline reads without clicks), and high societal stakes around misinformation, polarization, and public discourse. This special issue presents an opportunity to advance the field with rigorous methods and real-world relevance.
We’re especially excited about work on generative AI for news personalization, news recommendation on social media (including credibility and stakeholder trade-offs), multimodal signals, fake-news mitigation, dynamic/context-aware user modeling, richer contextual features, healthier interaction and commenting ecosystems, and evaluation/metrics beyond clicks.
Important dates:
- Submission deadline: March 15, 2026
- Author notifications: June 15, 2026
- Revisions due: August 15, 2026
- Final notification: October 15, 2026
Submissions go through Manuscript Central, and accepted papers will be published online immediately after acceptance.
Call for Papers (PDF): https://dl.acm.org/pb-assets/static_journal_pages/tors/pdf/ACM-TORS-CfP-New-Directions-News-Recommender-Systems-1764960204737.pdf
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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).