19 citations · 19 across the 4 of their papers we have counts for
4 papers
End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
Vladimir Baikalov, Evgeny Frolov
Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships withi…
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…
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…