DaQuaMRec
2nd International Workshop on
Data Quality-Aware Multimodal Recommendation
Held in conjunction with the 20th ACM Conference on Recommender Systems (RecSys 2026)
Embedded Files
📢 The Special Issue derived from the DaQuaMRec 2025 workshop, titled
"Special Issue on Challenges in Modern Multimodal Recommender Systems"
is now open in ACM TORS 📢
Click here to read the Call for Papers!
Multimodal recommender systems transform how we experience digital services, powering smarter suggestions in fashion, music, food, and beyond. Combining data from images, text, and audio, these systems outperform traditional, single-modality approaches, enabling richer user profiling and more accurate recommendations.
Driven by advances in deep learning and the rise of large foundation models, the multimodal recommendation is progressing remarkably. Yet, beneath the surface of these powerful models lies a crucial, often-overlooked challenge: data quality [Demartini et al., Wang et al.].
DaQuaMRec, the Second International Workshop on Data Quality-Aware Multimodal Recommendation, brings this foundational concern to the forefront. While state-of-the-art models continue to evolve, their performance and fairness are tightly bound to the quality of the data they rely on.
Noisy inputs [Zhang et al., Yu et al.], missing modality information [Ganhör et al., Malitesta et al. (2024)], misaligned data [Li et al. (2024), Xv et al.], and embedded biases [Liu et al., Malitesta et al. (2023)] can all degrade system performance and lead to inaccurate or unfair recommendations [Shang et al., Li et al. (2025)]. This workshop offers a dedicated space to explore these pressing issues.
Our mission is to foster deep, focused discussions and catalyze new research aimed at understanding, evaluating, and improving data quality in multimodal recommendation settings.
News
Stay tuned for the latest news and updates about DaQuaMRec.
[April 14, 2026]: Workshop website is online!
Topics of Interest
DaQuaMRec welcomes contributions on all topics related to data quality-aware multimodal recommendation,
focused (but not limited) to:
NOISY MULTI-MODAL DATA
INCOMPLETE OR MISSING MULTIMODAL DATA
BIAS IN MULTIMODAL DATA
PREFERENCE MISALIGNMENT ACROSS MODALITIES
FAIRNESS ISSUES IN MULTIMODAL RECOMMENDATION
ASSESSING MULTIMODAL DATA QUALITY IN RECOMMENDATION
Organisers
Claudio Pomo
Politecnico di Bari
Italy
Daniele Malitesta
Université Paris-Saclay
France
Alberto Carlo Maria Mancino
Politecnico di Bari
Italy
Marta Moscati
JKU Linz & Albatross AI
Austria
Dietmar Jannach
University of Klagenfurt
Austria
Yubin Kim
Vody, Inc.
USA
Aixin Sun
NTU Singapore
Singapore
Program committee
TBD
Contact Us
For any questions or inquiries, feel free to contact us at daquamrec@gmail.com
References
[Demartini et al.] 2026. Multimodal Data Quality – Human, Computational, and Institutional Perspectives. Seminar. Website: https://www.dagstuhl.de/26281
[Wang et al.] 2024. Data Quality-aware Graph Machine Learning. In CIKM. ACM, 5534–5537
[Zhang et al.] 2023. Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation. IEEE Trans. Knowl. Data Eng. 35, 9 (2023), 9154–9167
[Yu et al.] 2023. Multi-View Graph Convolutional Network for Multimedia Recommendation. In ACM Multi- media. ACM, 6576–6585
[Ganhör et al.] 2024. A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios. In RecSys. ACM, 380–390
[Malitesta et al. (2024)] 2024. Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?. In CIKM. ACM, 3943–3948
[Li et al. (2024)] 2024. Who To Align With: Feedback-Oriented Multi-Modal Alignment in Recommendation Systems. In SIGIR. ACM, 667–676
[Xv et al.] 2024. Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback. In KDD. ACM, 3645–3656
[Liu et al.] EliMRec: Eliminating Single-modal Bias in Multimedia Recommendation. In ACM Multimedia. ACM, 687–695
[Malitesta et al. (2023)] 2023. On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis. In MMIR@MM. ACM, 59–68
[Shang et al.] 2024. Improving Item-side Fairness of Multimodal Recommendation via Modality Debiasing. In WWW. ACM, 4697–4705
[Li et al. (2025)] 2025. Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations CoRR abs/2501.11916 (2025)
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