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Position Papers of the 21st Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 48

A Hybrid GraphRAG Approach to Generating Explainable Recommendations

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DOI: http://dx.doi.org/10.15439/2026F3730

Citation: Francisco Intriago-Usca,

Full text

Abstract. This paper proposes an architecture for hybrid recommender systems that integrates knowledge graphs and semantic representations of textual information. The architecture is based on the GraphRAG framework and has been designed with the aim of improving the relevance and explainability of recommendations by integrating knowledge graphs and large-scale language models. To validate the proposal, a prototype was implemented to provide recommendations related to the research of a specific topic. The functional prototype empirically validated the viability of the solution through a multidimensional evaluation, obtaining high levels of accuracy, relevance, and fidelity. The results showed that the integration of graphs and generative models significantly improves the quality, transparency, and reliability of recommendations, being a relevant contribution to the development of explainable recommendation systems.

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