A Personalized Recommender System Based-on Knowledge Graph Embeddings

TL;DR

This paper proposes a personalized recommender system using knowledge graph embeddings tailored for the vehicle purchase and sale domain, enhancing recommendation relevance by capturing user-item relationships.

Contribution

It introduces a novel approach applying knowledge graph embeddings specifically to vehicle recommender systems, improving personalization and recommendation accuracy.

Findings

Effective in providing relevant, personalized vehicle recommendations

Demonstrates improved accuracy over traditional methods

Validates approach through experimental results

Abstract

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.