51 citations · 110 across the 8 of their papers we have counts for
10 papers · 1 filter
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo +1
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While m…
A Topology-aware Analysis of Graph Collaborative Filtering
Daniele Malitesta, Claudio Pomo, Vito Walter Anelli +3
The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph…
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
Vito Walter Anelli, Daniele Malitesta, Claudio Pomo +3
The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph. How…
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation
Daniele Malitesta, Giuseppe Gassi, Claudio Pomo +1
In multimodal-aware recommendation, the extraction of meaningful multimodal features is at the basis of high-quality recommendations. Generally, each recommendation framework imple…
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
Federico Bianchi, Patrick John Chia, Ciro Greco +5
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a m…
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art
Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia +2
Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of acc…