Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantics beyond interaction patterns. Large Language Models (LLMs) present opportunities to enhance user understanding with their reasoning capabilities, yet existing integration approaches create prohibitive inference costs in real time. To address these limitations, we present a novel knowledge distillation method that utilizes textual user profile generated by pre-trained LLMs into sequential recommenders without requiring LLM inference at serving time. The resulting approach maintains the inference efficiency of traditional sequential models while requiring neither architectural modifications nor LLM fine-tuning.
@article{arxiv.2604.21536,
title = {Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation},
author = {Nikita Severin and Danil Kartushov and Vladislav Urzhumov and Vladislav Kulikov and Oksana Konovalova and Alexey Grishanov and Anton Klenitskiy and Artem Fatkulin and Alexey Vasilev and Andrey Savchenko and Ilya Makarov},
journal= {arXiv preprint arXiv:2604.21536},
year = {2026}
}
Comments
Accepted to ECIR 2026. 7 pages. This version of the contribution has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-032-21300-6_42