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stat.ML2026
Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization
Louise Davy, Stephan Clémençon, Charlotte Laclau
Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes…
stat.ML2017★ 38 cited
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii +3
In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing a pairwise ranking loss.…