4 papers
A Reproducible and Fair Evaluation of Partition-aware Collaborative Filtering
Domenico de Gioia, Claudio Pomo, Ludovico Boratto +1
Similarity-based collaborative filtering (CF) models have long demonstrated strong offline performance and conceptual simplicity. However, their scalability is limited by the quadr…
How Fair is Your Diffusion Recommender Model?
Daniele Malitesta, Giacomo Medda, Erasmo Purificato +3
Diffusion-based learning has settled as a rising paradigm in generative recommendation, outperforming traditional approaches built upon variational autoencoders and generative adve…
Fair Augmentation for Graph Collaborative Filtering
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emergin…
Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph
Giacomo Balloccu, Ludovico Boratto, Christian Cancedda +2
The integration of path reasoning with language modeling in recommender systems has shown promise for enhancing explainability but often struggles with the authenticity of the expl…