3 papers
cs.IR2026
Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
Nikita Severin, Danil Kartushov, Vladislav Urzhumov +8
Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantics beyond interaction patterns.…
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…
cs.IR2025
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…