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@article{192141,
        author = {Sonu G K and Dr Jithendra P R Nayak},
        title = {A Comprehensive Review of Knowledge Graph–Based Explainable Recommender Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {374-380},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192141},
        abstract = {In the big data era recommender systems are necessary because giving users relevant and tailored information across a variety of domains. Conventional recommendation techniques including content-based filtering collaborative filtering and hybrid models face difficulties with data sparsity cold start and interpretability. It has been demonstrated that comprehension graph-based recommender systems which use graph structures to incorporate strong semantic links between users items and features are an effective form of rehabilitation. By depicting entities and relationships as triples knowledge graphs increase the precision variety and relevance of recommendations. In order to learn higher-order relationships and adaptively aggregate neighbourhood information recent research combines Neural networks in graphs and graph attention networks. Knowledge graph-based recommendation models are categorized into embedded-based path-based and propagation-based models in this review study which also discusses their benefits and explain ability characteristics. Furthermore, in order to offer further thorough and reliable recommendations a unified framework incorporating item user and feature mediated explanation is emphasized. Scalable learning dynamic knowledge graphs standardized evaluation of explanations human-in-the-loop systems and fairness-aware recommender models are among the open research issues and feature research directions that are finally discussed.},
        keywords = {Knowledge graphs, collaborative filtering, content-based filtering, hybrid recommendation and recommender systems.},
        month = {February},
        }