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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.

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Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation

  • 会议论文

  • 发表日期：

    2026年

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  • Nikita Severin,
  • Danil Kartushov,
  • Vladislav Urzhumov,
  • Vladislav Kulikov,
  • Oksana Konovalova,
  • Alexey Grishanov,
  • Anton Klenitskiy,
  • Artem Fatkulin,
  • Alexey Vasilev,
  • Andrey Savchenko,
  • Ilya Makarov

摘要

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.