DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System

• URL: http://arxiv.org/abs/2408.08231v1
• Date: Thu, 15 Aug 2024 15:56:23 GMT
• Title: DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System
• Authors: Xihong Yang, Heming Jing, Zixing Zhang, Jindong Wang, Huakang Niu, Shuaiqiang Wang, Yu Lu, Junfeng Wang, Dawei Yin, Xinwang Liu, En Zhu, Defu Lian, Erxue Min,
• Abstract summary: Large language models (LLMs) have demonstrated remarkable performance in recommender systems. We propose a novel plug-and-play alignment framework for LLMs and collaborative models. Our method is superior to existing state-of-the-art algorithms.
• Score: 83.34921966305804
• License:
• Abstract: Benefiting from the strong reasoning capabilities, Large language models (LLMs) have demonstrated remarkable performance in recommender systems. Various efforts have been made to distill knowledge from LLMs to enhance collaborative models, employing techniques like contrastive learning for representation alignment. In this work, we prove that directly aligning the representations of LLMs and collaborative models is sub-optimal for enhancing downstream recommendation tasks performance, based on the information theorem. Consequently, the challenge of effectively aligning semantic representations between collaborative models and LLMs remains unresolved. Inspired by this viewpoint, we propose a novel plug-and-play alignment framework for LLMs and collaborative models. Specifically, we first disentangle the latent representations of both LLMs and collaborative models into specific and shared components via projection layers and representation regularization. Subsequently, we perform both global and local structure alignment on the shared representations to facilitate knowledge transfer. Additionally, we theoretically prove that the specific and shared representations contain more pertinent and less irrelevant information, which can enhance the effectiveness of downstream recommendation tasks. Extensive experimental results on benchmark datasets demonstrate that our method is superior to existing state-of-the-art algorithms.

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