DaQuaMRec welcomes contributions that advance our understanding of data quality issues in multimodal recommender systems. Submissions may focus on original research, reproducibility studies, tools, or datasets and visionary or critical perspectives on the field.

Topics of interest include, but are not limited to, methods for handling noisy or missing modality data, alignment techniques, bias detection and mitigation, and evaluation protocols that account for data quality. Both theoretical insights and practical approaches are encouraged across fashion, food, music, e-commerce, and more domains.

DaQuaMRec welcomes submissions that fall in the following three categories:

Research Papers: original work that has not been previously published, is not under review, and will not be submitted elsewhere during the review process.

• Long papers (up to 8 pages, excluding references) should make a clear and novel contribution to the field and be positioned in relation to the current state-of-the-art.

• Short papers (up to 4 pages, excluding references) may present early-stage research, promising ideas, or thought-provoking perspectives that are not yet mature enough for a long paper but can stimulate discussion and foster future work.
  
  

Reproducibility and Resource Papers: focused on tools, datasets, or reproducibility studies, including newly developed resources, major updates to existing tools, or systematic reproducibility evaluations.

• Long papers (up to 8 pages, excluding references) should present substantial contributions, such as comprehensive tools, large-scale datasets, or in-depth reproducibility analyses.

• Short papers (up to 4 pages, excluding references) may describe smaller-scale resources, preliminary reproducibility efforts, or focused tool descriptions that can benefit the community.
  
  

Position Papers: short, critical, or visionary contributions that highlight future directions, emerging challenges, or offer reflective perspectives on the state of the art. Submissions (up to 2 pages, excluding references) should aim to spark discussion and inspire future research, even without experimental results.

All submissions must be written in English, formatted as PDF files, and follow the CEUR-WS single-column conference format, available as a compressed archive and an Overleaf template.

Submissions will undergo a double-blind peer review process. Review criteria include relevance to the workshop, originality, significance of the contribution, technical soundness, clarity of presentation, quality of references, and reproducibility.
Authors are encouraged to share code and supplementary material via an anonymous repository, such as https://anonymous.4open.science/, to support reproducibility.

Submissions that are not properly anonymized, fail to follow the required formatting, or disregard these guidelines may be rejected without review.

Accepted long and short papers will be published in the CEUR Workshop Proceedings and presented in the main workshop program. Position papers will also be included in the proceedings; a selection of these may be invited for oral presentations.

Materials and workshop discussions will be available on this website.

Please be aware that at least one author per paper needs to register and attend the workshop to present the work.
We expect authors, the program committee, and the organizing committee to adhere to the ACM's Conflict of Interest Policy and the ACM's Code of Ethics and Professional Conduct.

We're currently in tight contact with the Editors in Chief of 𝗔𝗖𝗠 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗼𝗻 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗧𝗢𝗥𝗦) discussing the proposal of a 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 on similar (and more broader) topics discussed at DaQuaMRec. Stay tuned 🚀🚀