Knowledge graphs have proven to be effective for modeling entities and their relationships through the use of ontologies. The recent emergence in interest for using knowledge graphs as a form of information modeling has led to their increased adoption in recommender systems. By incorporating users and items into the knowledge graph, these systems can better capture the implicit connections between them and provide more accurate recommendations. In this paper, we investigate and propose the construction of a personalized recommender system via knowledge graphs embedding applied to the vehicle purchase/sale domain. The results of our experimentation demonstrate the efficacy of the proposed method in providing relevant recommendations that are consistent with individual users.

翻译：知识图谱通过本体建模已被证明能有效表示实体及其相互关系。近年来，将知识图谱作为信息建模形式的研究兴趣激增，推动其在推荐系统中的广泛应用。通过将用户和物品纳入知识图谱，这些系统能够更精准地捕捉用户与物品间的隐含关联，从而提供更准确的推荐。本文针对车辆交易领域，研究并提出一种基于知识图谱嵌入的个性化推荐系统构建方法。实验结果表明，该方法在提供与用户个体特征相符的相关推荐方面具有显著有效性。