Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?

• URL: http://arxiv.org/abs/2502.13909v1
• Date: Wed, 19 Feb 2025 17:41:09 GMT
• Title: Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?
• Authors: Sein Kim, Hongseok Kang, Kibum Kim, Jiwan Kim, Donghyun Kim, Minchul Yang, Kwangjin Oh, Julian McAuley, Chanyoung Park,
• Abstract summary: Large Language Models (LLMs) have emerged as promising tools for recommendation thanks to their advanced textual understanding ability and context-awareness. We propose a method that enhances the integration of sequential information into LLMs by distilling the user representations extracted from a pre-trained-SRec model into LLMs. Our experiments show that LLM-SRec enhances LLMs' ability to understand users' item interaction sequences, ultimately leading to improved recommendation performance.
• Score: 33.92662524009036
• License:
• Abstract: Large Language Models (LLMs) have recently emerged as promising tools for recommendation thanks to their advanced textual understanding ability and context-awareness. Despite the current practice of training and evaluating LLM-based recommendation (LLM4Rec) models under a sequential recommendation scenario, we found that whether these models understand the sequential information inherent in users' item interaction sequences has been largely overlooked. In this paper, we first demonstrate through a series of experiments that existing LLM4Rec models do not fully capture sequential information both during training and inference. Then, we propose a simple yet effective LLM-based sequential recommender, called LLM-SRec, a method that enhances the integration of sequential information into LLMs by distilling the user representations extracted from a pre-trained CF-SRec model into LLMs. Our extensive experiments show that LLM-SRec enhances LLMs' ability to understand users' item interaction sequences, ultimately leading to improved recommendation performance. Furthermore, unlike existing LLM4Rec models that require fine-tuning of LLMs, LLM-SRec achieves state-of-the-art performance by training only a few lightweight MLPs, highlighting its practicality in real-world applications. Our code is available at https://github.com/Sein-Kim/LLM-SRec.

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