MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation
This work addresses the need for more transparent and personalized explainable recommendations by incorporating multimodal information, which is often ignored in existing LLM-based methods.
MMP-Refer introduces a multimodal retrieval path framework for explainable recommendations using LLMs, achieving improved personalization and accuracy by integrating multimodal embeddings and a collaborative adapter. Experiments show effectiveness over baselines.
Explainable recommendations help improve the transparency and credibility of recommendation systems, and play an important role in personalized recommendation scenarios. At present, methods for explainable recommendation based on large language models(LLMs) often consider introducing collaborative information to enhance the personalization and accuracy of the model, but ignore the multimodal information in the recommendation dataset; In addition, collaborative information needs to be aligned with the semantic space of LLM. Introducing collaborative signals through retrieval paths is a good choice, but most of the existing retrieval path collection schemes use the existing Explainable GNN algorithms. Although these methods are effective, they are relatively unexplainable and not be suitable for the recommendation field. To address the above challenges, we propose MMP-Refer, a framework using \textbf{M}ulti\textbf{M}odal Retrieval \textbf{P}aths with \textbf{Re}trieval-augmented LLM \textbf{F}or \textbf{E}xplainable \textbf{R}ecommendation. We use a sequential recommendation model based on joint residual coding to obtain multimodal embeddings, and design a heuristic search algorithm to obtain retrieval paths by multimodal embeddings; In the generation phase, we integrated a trainable lightweight collaborative adapter to map the graph encoding of interaction subgraphs to the semantic space of the LLM, as soft prompts to enhance the understanding of interaction information by the LLM. Extensive experiments have demonstrated the effectiveness of our approach. Codes and data are available at https://github.com/pxcstart/MMP-Refer.