Second International Workshop on Data Quality-Aware Multimodal Recommendation (DaQuaMRec)

Abstract

The Second International Workshop on Data Quality-Aware Multimodal Recommendation (DaQuaMRec) addresses the increasingly important challenge of understanding how multimodal data quality affects the robustness, fairness, explainability, and practical utility of modern recommender systems. As multimodal recommendation evolves through deep representation learning, graph-based architectures, self-supervision, and foundation-model-based pipelines, the focus cannot remain solely on building stronger models. Increasing attention must also be devoted to the quality, completeness, alignment, and reliability of the multimodal data on which these systems depend. Recent studies have highlighted the impact of noisy signals, missing modalities, semantic misalignment, low-quality features, and multimodal bias, showing that these issues directly influence both system performance and trustworthiness. Building on the success of the first DaQuaMRec workshop at ACM RecSys 2025, this second edition aims to consolidate a research community around these emerging challenges, stimulate work on their assessment and mitigation, and promote reproducible evaluation practices. The workshop will combine invited talks, paper presentations, and interactive discussion sessions to foster collaboration across academia and industry and to articulate concrete future directions for data-centric multimodal recommendation.