Collaborative Cross-modal Fusion with Large Language Model for Recommendation
This work addresses the problem of integrating collaborative filtering with semantic knowledge for recommendation systems, representing an incremental improvement in the LLM4Rec domain.
The paper tackles the limitation of existing large language model-based recommendation methods that overlook collaborative signals by proposing CCF-LLM, a framework that fuses semantic knowledge and collaborative signals through hybrid prompts and attentive cross-modal fusion, achieving superior performance over existing methods in experiments.
Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent focus on the application of large language models for recommendation (LLM4Rec) has highlighted their capability for effective semantic knowledge capture. However, these methods often overlook the collaborative signals in user behaviors. Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, for recommendation. In this framework, we translate the user-item interactions into a hybrid prompt to encode both semantic knowledge and collaborative signals, and then employ an attentive cross-modal fusion strategy to effectively fuse latent embeddings of both modalities. Extensive experiments demonstrate that CCF-LLM outperforms existing methods by effectively utilizing semantic and collaborative signals in the LLM4Rec context.