8 papers
SplitLight: An Exploratory Toolkit for Recommender Systems Datasets and Splits
Anna Volodkevich, Dmitry Anikin, Danil Gusak +3
Offline evaluation of recommender systems is often affected by hidden, under-documented choices in data preparation. Seemingly minor decisions in filtering, handling repeats, cold-…
Scalable LinUCB: Low-Rank Design Matrix Updates for Recommenders with Large Action Spaces
Evgenia Shustova, Marina Sheshukova, Sergey Samsonov +1
In this paper, we introduce PSI-LinUCB, a scalable variant of LinUCB that enables efficient training, inference, and memory usage by representing the inverse regularized design mat…
Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation
Askar Tsyganov, Evgeny Frolov, Sergey Samsonov +1
In this paper, we propose new randomized algorithms for estimating the two-to-infinity and one-to-two norms in a matrix-free setting, using only matrix-vector multiplications. Our…
Barlow Twins for Sequential Recommendation
Ivan Razvorotnev, Marina Munkhoeva, Evgeny Frolov
Sequential recommendation models must navigate sparse interaction data popularity bias and conflicting objectives like accuracy versus diversity While recent contrastive selfsuperv…
Ultra Fast Warm Start Solution for Graph Recommendations
Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov
In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely imp…
Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models
Tatyana Matveeva, Aleksandr Katrutsa, Evgeny Frolov
Adaptive gradient methods like Adagrad and its variants are widespread in large-scale optimization. However, their use of diagonal preconditioning matrices limits the ability to ca…