2 papers
cs.IR2026
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…
cs.IR2025
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…