We describe the PoliBaJukebox submission to the ACM RecSys Challenge 2026 on conversational music recommendation over the TalkPlayData 2 corpus. Our system implements a modular two-stage pipeline: a first stage retrieves candidates from the full track catalog using ten heterogeneous sources, fuses them with weighted Re...
Andrea Lops, Nicola Cipriani, Gabriele Colapinto et al.· Proceedings of the Workshop...· 1 citation
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
Offline evaluation is the dominant experimental paradigm in recommender systems research, enabling reproducible and cost-effective comparisons on historical interaction data. Yet, while considerable attention has been devoted to recommendation models and evaluation methodologies, the data processing decisions that prec...
Alberto Carlo Maria Mancino, Angela Di Fazio, Danilo Danese et al.· 0 citations
Experimental results show the pipeline outperforms baselines in ranking, diversity, and explanations, proving that combining graph embeddings with agentic reasoning successfully bridges long-term profiles and immediate intent.
Marco Valentini, Bianca Di Bitetto, Gianmichele De Palma et al.· Proceedings of the Workshop...· 1 citation
BACKGROUND
Graph-based machine learning approaches, including Knowledge Graph Embedding (KGE) methods and Graph Neural Networks (GNNs), have emerged as powerful tools for modeling complex biomedical data. However, a systematic and clinically grounded comparison of these approaches across heterogeneous healthcare graphs...
P. Sorino, A. D. Bellis, Daniele Malitesta et al.· BMC Medical Informatics and...· 0 citations
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