29 citations · 102 across the 13 of their papers we have counts for
15 papers
Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers
Andrei Chertkov, Artem Basharin, Mikhail Saygin +3
The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photonic, neuromorphic) due to their p…
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs
Gleb Mezentsev, Danil Gusak, Ivan Oseledets +1
Scalability issue plays a crucial role in productionizing modern recommender systems. Even lightweight architectures may suffer from high computational overload due to intermediate…
RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders
Danil Gusak, Gleb Mezentsev, Ivan Oseledets +1
Scalability is a major challenge in modern recommender systems. In sequential recommendations, full Cross-Entropy (CE) loss achieves state-of-the-art recommendation quality but con…
Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems
Albert Saiapin, Ivan Oseledets, Evgeny Frolov
In production applications of recommender systems, a continuous data flow is employed to update models in real-time. Many recommender models often require complete retraining to ad…
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction
Albert Sayapin, Gleb Balitskiy, Daniel Bershatsky +5
In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a class…
Mitigating Human and Computer Opinion Fraud via Contrastive Learning
Yuliya Tukmacheva, Ivan Oseledets, Evgeny Frolov
We introduce the novel approach towards fake text reviews detection in collaborative filtering recommender systems. The existing algorithms concentrate on detecting the fake review…
Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations
Evgeny Frolov, Ivan Oseledets
Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently. The learned attention weights capture sequential dynamics in…
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll +2
Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario coll…
Tensor-based Collaborative Filtering With Smooth Ratings Scale
Nikita Marin, Elizaveta Makhneva, Maria Lysyuk +3
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items whi…
Dynamic Modeling of User Preferences for Stable Recommendations
Oluwafemi Olaleke, Ivan Oseledets, Evgeny Frolov
In domains where users tend to develop long-term preferences that do not change too frequently, the stability of recommendations is an important factor of the perceived quality of…
Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
Leyla Mirvakhabova, Evgeny Frolov, Valentin Khrulkov +2
We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem. In contrast to many modern deep learning techniques, we build o…
Revealing the Unobserved by Linking Collaborative Behavior and Side Knowledge
Evgeny Frolov, Ivan Oseledets
We propose a tensor-based model that fuses a more granular representation of user preferences with the ability to take additional side information into account. The model relies on…
HybridSVD: When Collaborative Information is Not Enough
Evgeny Frolov, Ivan Oseledets
We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique. One of its key features is th…
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks
Evgeny Frolov, Ivan Oseledets
Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. This formulation, however, disregar…
Tensor Methods and Recommender Systems
Evgeny Frolov, Ivan Oseledets
A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Ten…