mlsanityData to 14 Oct 2026

A Personalized Recommender System Based-on Knowledge Graph Embeddings

arXiv 2307.10680 ↗First posted 20 Jul 2023cs.AI cs.IRcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 20 Jul 2023.

Builds on

4 of 24 references.

References · 4

  1. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  3. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  4. 2022Apport des ontologies pour le calcul de la similarit'e s'emantique au sein d'un syst`eme de recommandationcited by 2

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025From Individual to Group: Developing a Context-Aware Multi-Criteria Group Recommender Systemcited by 0
  2. 2025Joint Group Profiling and Recommendation via Deep Neural Network-based Multi-Task Learningcited by 0
  3. 2025Enhancing Recommender Systems Using Textual Embeddings from Pre-trained Language Modelscited by 0