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.

翻译：可解释推荐有助于提升推荐系统的透明度和可信度，在个性化推荐场景中发挥重要作用。当前基于大语言模型（LLMs）的可解释推荐方法通常考虑引入协同信息以增强模型的个性化和准确性，但忽略了推荐数据集中的多模态信息；此外，协同信息需与大语言模型的语义空间对齐。通过检索路径引入协同信号是一个较好的选择，但现有检索路径收集方案多使用现有的可解释图神经网络算法。尽管这些方法有效，但其本身相对不可解释，且不适用于推荐领域。针对上述挑战，我们提出MMP-Refer框架，即利用**多模态检索路径**与检索增强大语言模型实现**可解释推荐**。我们采用基于联合残差编码的序列推荐模型获取多模态嵌入，并设计启发式搜索算法通过多模态嵌入获取检索路径；在生成阶段，我们集成了一个可训练的轻量级协同适配器，将交互子图的图编码映射至大语言模型的语义空间，作为软提示以增强大语言模型对交互信息的理解。大量实验证明了我们方法的有效性。代码与数据可在https://github.com/pxcstart/MMP-Refer获取。