A Constraint-based Recommender System via RDF Knowledge Graphs

TL;DR

This paper presents a novel approach to vehicle purchase/sale recommendations by integrating constraint-based methods with RDF knowledge graphs, enhancing recommendation relevance and efficiency.

Contribution

It introduces a constraint-based recommender system leveraging RDF knowledge graphs specifically for vehicle transactions, which is a new application of this integration.

Findings

Efficiently identifies recommendations aligned with user preferences.

Utilizes RDF knowledge graphs to model complex relationships in vehicle domain.

Improves recommendation relevance through constraint-based reasoning.

Abstract

Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users and items. Constraint-based recommender systems are based on the idea of explicitly exploiting deep recommendation knowledge through constraints to identify relevant recommendations. When combined with knowledge graphs, a constraint-based recommender system gains several benefits in terms of constraint sets. In this paper, we investigate and propose the construction of a constraint-based recommender system via RDF knowledge graphs applied to the vehicle purchase/sale domain. The results of our experiments show that the proposed approach is able to efficiently identify recommendations in accordance with user preferences.