23 citations · 24 across the 3 of their papers we have counts for
4 papers · 1 filter
Mitigating Popularity Bias in Sequential Recommendations with Sparse Prior-Regularized Objective
Anton Pembek, Anton Klenitskiy, Alexander Savchenko +1
Popularity bias is a well-known challenge in recommender systems, often leading to the over-exposure of popular items and reduced catalog coverage. In this work, we propose PR-entm…
Encode Me If You Can: Learning Universal User Representations via Event Sequence Autoencoding
Anton Klenitskiy, Artem Fatkulin, Daria Denisova +2
Building universal user representations that capture the essential aspects of user behavior is a crucial task for modern machine learning systems. In real-world applications, a use…
Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization
Anton Pembek, Artem Fatkulin, Anton Klenitskiy +1
Many sequential recommender systems suffer from the cold start problem, where items with few or no interactions cannot be effectively used by the model due to the absence of a trai…
Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations
Anton Klenitskiy, Anna Volodkevich, Anton Pembek +1
Sequential recommender systems are an important and demanded area of research. Such systems aim to use the order of interactions in a user's history to predict future interactions.…