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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.…
stat.ML2016
Exponential Machines
Alexander Novikov, Mikhail Trofimov, Ivan Oseledets
Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we in…