38 citations · 38 across the 4 of their papers we have counts for
4 papers
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…
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
Aleksandra Burashnikova, Yury Maximov, Marianne Clausel +3
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012.06910], where we proposed a theoretically supported sequential strategy for training a large-scale Reco…
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation
Aleksandra Burashnikova, Marianne Clausel, Charlotte Laclau +3
In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks. The pr…
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.…