The RecSys Challenge 2026 focuses on the evolving landscape of music discovery, where static recommendation lists are being replaced by dynamic, conversational interactions. As users increasingly interact with AI through natural language, there is a critical need for systems that can seamlessly integrate Natural Langua...
Seungheon Doh, Sergio Oramas, B. Sguerra et al.· Proceedings of the 20th ACM...· 1 citation
The Third Workshop on Agentic and Generative AI for E-Commerce, co-located with the 20th ACM Conference on Recommender Systems (RecSys 2026), brings together researchers and practitioners to examine the rapidly evolving intersection of recommender systems, generative AI, and agentic AI in online retail. As AI systems e...
M. Mane, Neeti Narayan, Djordje Gligorijevic et al.· Proceedings of the 20th ACM...· 0 citations
Over the past two decades, Recommender Systems (RSs) have repeatedly changed how they integrate external knowledge, moving from constraints and side information to Knowledge Graphs (KG) and, more recently, Large Language Models (LLMs). This Past/Present/Future retrospective interprets that evolution as a sequence of re...
Claudio Pomo, Diego Baquero Sanz, Liam Claude Morris et al.· Proceedings of the 20th ACM...· 0 citations
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 ev...
Claudio Pomo, Daniele Malitesta, Alberto Carlo Maria Mancino et al.· Proceedings of the 20th ACM...· 0 citations
A systematic, cross-domain characterisation of data processing practices for the offline evaluation of recommender systems is provided, identifying substantial heterogeneity in how auxiliary information is prepared and represented, as well as inconsistencies in the specification of data splitting protocols.
Alberto Carlo Maria Mancino, Angela Di Fazio, Danilo Danese et al.· 0 citations
PUMA, a sparse autoencoder recipe that maps universal multimodal embeddings to compact sparse codes without retraining the backbone, reduces vector storage by 8-16x (FP32) and is up to 25x faster than exact dense scoring on larger candidate pools, enabling efficient multimodal retrieval.
Matteo Attimonelli, Alessandro De Bellis, Franco Maria Nardini et al.· 0 citations
This paper presents the RecSys Challenge 2026 challenge task, dataset, evaluation protocol, and official results, and identifies limitations of the benchmark and evaluation protocol, including single-ground-truth relevance and teacher-forced evaluation of synthetic dialogues.
Seungheon Doh, Sergio Oramas, B. Sguerra et al.· Proceedings of the Workshop...· 0 citations
StyleFlow instantiates the task with StyleFlow, a Rectified Flow Matching model that jointly conditions on the seed image and instruction within a single multimodal transformer, and consistently produces instruction-aligned and stylistically coherent garments while reducing architectural complexity and inference cost r...
Matteo Attimonelli, Claudio Pomo, Alessandro De Bellis et al.· 0 citations
A reproducibility and replicability study that examines three major families of graph models, showing that strong classical methods remain highly competitive and that no single spectral model dominates across domains, and finds that spectral filtering often improves catalog exploration even when accuracy gains are marg...
Domenico de Gioia, Claudio Pomo, Ludovico Boratto et al.· Annual International ACM SIG...· 1 citation
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