3 papers
cs.IR2026
Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control
Anton Klenitskiy, Konstantin Polev, Daria Denisova +3
Many current state-of-the-art models for sequential recommendations are based on transformer architectures. Interpretation and explanation of such black box models is an important…
cs.LG2025
Topological Metric for Unsupervised Embedding Quality Evaluation
Aleksei Shestov, Anton Klenitskiy, Daria Denisova +4
Modern representation learning increasingly relies on unsupervised and self-supervised methods trained on large-scale unlabeled data. While these approaches achieve impressive gene…
cs.IR2025
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…