Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation

TL;DR

This paper introduces a novel information sharing module that bridges domain-specific models and general LLMs to enhance personalized recommendation performance by leveraging mutual knowledge exchange.

Contribution

It proposes an information sharing mechanism enabling collaborative training between LLMs and domain-specific models, improving recommendation accuracy and addressing data sparsity issues.

Findings

Improved recommendation performance on three real-world datasets.

Enhanced knowledge transfer between LLMs and domain models.

Effective handling of community behavior patterns in recommendations.

Abstract

Generative large language models(LLMs) are proficient in solving general problems but often struggle to handle domain-specific tasks. This is because most of domain-specific tasks, such as personalized recommendation, rely on task-related information for optimal performance. Current methods attempt to supplement task-related information to LLMs by designing appropriate prompts or employing supervised fine-tuning techniques. Nevertheless, these methods encounter the certain issue that information such as community behavior pattern in RS domain is challenging to express in natural language, which limits the capability of LLMs to surpass state-of-the-art domain-specific models. On the other hand, domain-specific models for personalized recommendation which mainly rely on user interactions are susceptible to data sparsity due to their limited common knowledge capabilities. To address these issues, we proposes a method to bridge the information gap between the domain-specific models and the general large language models. Specifically, we propose an information sharing module which serves as an information storage mechanism and also acts as a bridge for collaborative training between the LLMs and domain-specific models. By doing so, we can improve the performance of LLM-based recommendation with the help of user behavior pattern information mined by domain-specific models. On the other hand, the recommendation performance of domain-specific models can also be improved with the help of common knowledge learned by LLMs. Experimental results on three real-world datasets have demonstrated the effectiveness of the proposed method.