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提升推荐系统透明度的潜力而受到广泛关注。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之间的相互依赖性进行推荐，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。该方法使知识图谱嵌入由语言模型直接从知识图谱中的路径学习得到，同时将实体和关系统一到同一优化空间中。序列解码约束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表明，与最先进的基线方法相比，本方法具有有效性。源代码与数据集：论文接收后提供。