39 citations · 39 across the 4 of their papers we have counts for
5 papers · 1 filter
Calibrating Reproduced Claims in Recommender Systems
Alan Said
Reproduction studies can produce mixed outcomes. Reported values may differ while the ordering of the compared methods remains the same, a result may hold only under some experimen…
Reproducibility in Recommender Systems: A Survey
Alan Said, Alejandro Bellogin
Reproducibility has become a cornerstone of credible recommender systems research, driven by growing concerns about the reliability and generalizability of experimental results. In…
Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
Lukas Wegmeth, Tobias Vente, Alan Said +1
As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems commun…
EMERS: Energy Meter for Recommender Systems
Lukas Wegmeth, Tobias Vente, Alan Said +1
Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems communit…
From Clicks to Carbon: The Environmental Toll of Recommender Systems
Tobias Vente, Lukas Wegmeth, Alan Said +1
As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address thei…