A Personalized Recommender System Based-on Knowledge Graph Embeddings
This is an incremental improvement for users in the vehicle purchase/sale domain, as it applies an existing method to a new dataset.
The paper tackled the problem of improving recommendation accuracy by constructing a personalized recommender system using knowledge graph embeddings in the vehicle purchase/sale domain, and the results demonstrated its efficacy in providing relevant recommendations consistent with individual users.
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.