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.