3 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.IR2025★ 3 cited
Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch
Michael Benigni, Maurizio Ferrari Dacrema, Dietmar Jannach
Countless new machine learning models are published every year and are reported to significantly advance the state-of-the-art in top-n recommendation. However, earlier reproducibil…
cs.IR2025★ 2 cited
Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing
Maurizio Ferrari Dacrema, Michael Benigni, Nicola Ferro
Graph-based techniques relying on neural networks and embeddings have gained attention as a way to develop Recommender Systems (RS) with several papers on the topic presented at SI…