Path reasoning methods over knowledge graphs have gained popularity for their potential to improve transparency in recommender systems. However, the resulting models still rely on pre-trained knowledge graph embeddings, fail to fully exploit the interdependence between entities and relations in the KG for recommendation, and may generate inaccurate explanations. In this paper, we introduce PEARLM, a novel approach that efficiently captures user behaviour and product-side knowledge through language modelling. With our approach, knowledge graph embeddings are directly learned from paths over the KG by the language model, which also unifies entities and relations in the same optimisation space. Constraints on the sequence decoding additionally guarantee path faithfulness with respect to the KG. Experiments on two datasets show the effectiveness of our approach compared to state-of-the-art baselines. Source code and datasets: AVAILABLE AFTER GETTING ACCEPTED.

翻译：基于知识图谱的路径推理方法因其在提升推荐系统透明度方面的潜力而日益受到关注。然而，现有模型仍依赖预训练的知识图谱嵌入，未能充分利用知识图谱中实体与关系间的相互依赖关系进行推荐，且可能生成不准确的解释。本文提出PEARLM——一种通过语言建模高效捕捉用户行为与产品侧知识的新方法。该方法使语言模型直接从知识图谱路径中学习嵌入表示，并在同一优化空间中统一处理实体与关系。序列解码中的约束机制进一步保证了路径相对于知识图谱的忠实性。在两个数据集上的实验结果表明，本方法相较当前最优基线具有显著有效性。源代码与数据集将在论文被接收后公开。