Recommender systems are pivotal in enhancing user experiences across various web applications by analyzing the complicated relationships between users and items. Knowledge graphs(KGs) have been widely used to enhance the performance of recommender systems. However, KGs are known to be noisy and incomplete, which are hard to provide reliable explanations for recommendation results. An explainable recommender system is crucial for the product development and subsequent decision-making. To address these challenges, we introduce a novel recommender that synergies Large Language Models (LLMs) and KGs to enhance the recommendation and provide interpretable results. Specifically, we first harness the power of LLMs to augment KG reconstruction. LLMs comprehend and decompose user reviews into new triples that are added into KG. In this way, we can enrich KGs with explainable paths that express user preferences. To enhance the recommendation on augmented KGs, we introduce a novel subgraph reasoning module that effectively measures the importance of nodes and discovers reasoning for recommendation. Finally, these reasoning paths are fed into the LLMs to generate interpretable explanations of the recommendation results. Our approach significantly enhances both the effectiveness and interpretability of recommender systems, especially in cross-selling scenarios where traditional methods falter. The effectiveness of our approach has been rigorously tested on four open real-world datasets, with our methods demonstrating a superior performance over contemporary state-of-the-art techniques by an average improvement of 12%. The application of our model in a multinational engineering and technology company cross-selling recommendation system further underscores its practical utility and potential to redefine recommendation practices through improved accuracy and user trust.

翻译：推荐系统通过分析用户与物品间复杂关系，在各类网络应用中发挥着提升用户体验的关键作用。知识图谱已被广泛用于增强推荐系统性能，但其固有的噪声与不完整性难以提供可靠的推荐解释。可解释推荐系统对产品开发与后续决策至关重要。为应对这些挑战，我们提出一种融合大语言模型与知识图谱的新型推荐器，以提升推荐效果并提供可解释结果。具体而言，我们首先利用LLM增强知识图谱重构：通过解析用户评论并将其解构为新增三元组注入知识图谱，从而构建蕴含用户偏好的可解释路径以丰富知识图谱。为在增强知识图谱上优化推荐，我们设计了一种创新的子图推理模块，能有效评估节点重要性并发现推荐推理依据。最终，这些推理路径被输入LLM以生成可解释的推荐结果说明。我们的方法显著提升了推荐系统的效能与可解释性，尤其在传统方法失效的交叉销售场景中表现突出。通过在四个公开真实数据集上的严格测试，本方法相较当前最优技术平均提升12%的性能表现。在跨国工程科技公司的交叉销售推荐系统中，本模型通过提升准确率与用户信任度，进一步彰显了其重塑推荐实践的实际效用与潜力。