3 citations · 3 across the 5 of their papers we have counts for
5 papers
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.…
Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models
Harry Proshian, Nikita Severin, Sergey Nikolenko +5
Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recomm…
ATGen: A Framework for Active Text Generation
Akim Tsvigun, Daniil Vasilev, Ivan Tsvigun +12
Active learning (AL) has demonstrated remarkable potential in reducing the annotation effort required for training machine learning models. However, despite the surging popularity…
LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering
Nikita Severin, Aleksei Ziablitsev, Yulia Savelyeva +8
We present LLM-KT, a flexible framework designed to enhance collaborative filtering (CF) models by seamlessly integrating LLM (Large Language Model)-generated features. Unlike exis…
The Good, the Bad, and the Hulk-like GPT: Analyzing Emotional Decisions of Large Language Models in Cooperation and Bargaining Games
Mikhail Mozikov, Nikita Severin, Valeria Bodishtianu +4
Behavior study experiments are an important part of society modeling and understanding human interactions. In practice, many behavioral experiments encounter challenges related to…