[CFP] ACM TORS Special Issue on Challenges in Modern Multimodal Recommender Systems

ACM Transactions on Recommender Systems (TORS)
Special Issue on Challenges in Modern Multimodal Recommender Systems

https://dl.acm.org/pb-assets/static_journal_pages/tors/pdf/ACM-TORS-CFP-C=
hallenges-MMRS-1771613841820.pdf

ACM Transactions on Recommender Systems (TORS) invites submissions to a S=
pecial Issue on *Challenges in Modern Multimodal Recommender Systems*.

Recommender systems have been increasingly shaped by multimodal approache=
s that combine multiple types of data and content, including text, images=
, audio, video, structured metadata, knowledge graphs, and behavioral or =
sensor signals. Modern multimodal recommender systems are now central to =
major application domains such as e-commerce, entertainment, news and med=
ia platforms, social networks, and personalized content delivery.

Despite recent progress, important challenges remain in the design, deplo=
yment, and evaluation of modern multimodal recommender systems. These inc=
lude noisy, incomplete, or biased multimodal data; effective fusion of he=
terogeneous modalities; robustness under modality drops and distribution =
shifts; the relationship between multimodal retrieval and personalized ra=
nking; evaluation and benchmarking beyond static accuracy metrics; online=
 evaluation and feedback loops; fairness, privacy, copyright, and safety =
issues; and the role of foundation models and generative AI in recommenda=
tion pipelines.

The special issue welcomes contributions on topics including, but not lim=
ited to:

* Data-centric challenges in multimodal recommender systems
* Modeling and algorithmic innovations for multimodal recommendation
* Multimodal candidate retrieval and personalized ranking
* Evaluation, benchmarking, and user-centric assessment
* Online evaluation, feedback loops, and bandit-based approaches
* Deployment, monitoring, and MLOps for multimodal recommenders
* Human-centered, ethical, and responsible recommendation
* Foundation models and generative AI for multimodal recommendation

TORS welcomes different types of submissions, including technical researc=
h papers, theoretical or methodological studies, applied case studies fro=
m industry, survey and review articles, and perspective/opinion pieces. E=
xtended versions of relevant conference papers are also encouraged.

Important Dates

* Submission deadline: June 1, 2026
* First-round review decisions: August 1, 2026
* Revision submission deadline: October 1, 2026
* Final decision notification: February 1, 2027

Guest Editors

* Yubin Kim (Vody)
* Daniele Malitesta (Universit=C3=A9 Paris-Saclay)
* Alberto Carlo Maria Mancino (Politecnico di Bari)
* Claudio Pomo (Politecnico di Bari)
* Shah Nawaz (Johannes Kepler University Linz)

For further information, please refer to the CFP at the link above.

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