3 citations · 6 across the 3 of their papers we have counts for
4 papers
Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling
Nicolò Felicioni, Michael Benigni, Maurizio Ferrari Dacrema +1
Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly robust (DR) estimation remai…
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…
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…
Automated Off-Policy Estimator Selection via Supervised Learning
Nicolò Felicioni, Michael Benigni, Maurizio Ferrari Dacrema
The Off-Policy Evaluation (OPE) problem consists of evaluating the performance of counterfactual policies with data collected by another one. To solve the OPE problem, we resort to…