# A Constraint-based Recommender System via RDF Knowledge Graphs

arXiv: https://arxiv.org/abs/2307.10702 · HTML: https://mlsanity.com/paper/2307.10702/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2023.

## Built on by (most cited first, 5 of 5)

- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation](https://mlsanity.com/paper/2511.16478/) (2025)
- [Development of Ontological Knowledge Bases by Leveraging Large Language Models](https://mlsanity.com/paper/2601.10436/) (2026)
- [From Individual to Group: Developing a Context-Aware Multi-Criteria Group Recommender System](https://mlsanity.com/paper/2503.22752/) (2025)
- [Enhancing Recommender Systems Using Textual Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2504.08746/) (2025)

## Abstract (arXiv)

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.