Authors - Ronky Amber-Doh, Benjamin Ghansah, Winfred Larkotey, Stephen Opoku Oppong, Ezekiel Okoe, Olivia Osei-Tutu, Emmanuel Prah, Ephrem Kwaa-Aidoo Abstract - This paper introduces a novel framework, ExplainoGraph, that integrates square loss optimization with explainable artificial intelligence methods for knowledge graph–based recommender systems. Prior studies show that knowledge graph embeddings significantly enhance recommendation accuracy; however, they suffer from limited interpretability, thereby constraining user trust and system transparency. To address this gap, we introduce ExplainoGraph, which embeds explainability directly into the recommendation process through interpretable scoring functions and feature attribution procedures that provide meaningful insights into model decisions. Again, the framework incorporates ripple set propagation to effectively model user preferences, particularly in sparse data environments where traditional methods are suboptimal. Extensive experiments conducted on multiple benchmark datasets demonstrate that Explaino-Graph consistently outperforms the state-of-the-art baselines used across key evaluation metrics, including Precision@K, Recall@K, F1-score, and normalized discounted cumulative gain (NDCG)
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