Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning
Abstract Explainable Recommender Systems (XRSs) assist users in decision-making by offering precise and interpretable recommendations. However, most existing XRSs tend to prioritise either accuracy or explainability in isolation, with limited attention to aligning recommendations with individual use...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00173-5 |
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| author | Neeraj Tiwary Shahrul Azman Mohd Noah Fariza Fauzi |
| author_facet | Neeraj Tiwary Shahrul Azman Mohd Noah Fariza Fauzi |
| author_sort | Neeraj Tiwary |
| collection | DOAJ |
| description | Abstract Explainable Recommender Systems (XRSs) assist users in decision-making by offering precise and interpretable recommendations. However, most existing XRSs tend to prioritise either accuracy or explainability in isolation, with limited attention to aligning recommendations with individual user preferences while providing well-substantiated justifications. To address this gap, we propose the Explainable Artificial Intelligence-driven Recommender System (XAIRec), which incorporates explainability mechanisms into knowledge graphs (KGs) and leverages knowledge graph embeddings (KGEs) alongside reinforcement learning (RL) to enhance both transparency and interpretability. By applying optimisation techniques that consider rewards, probabilities, information values, and affinity scores while minimising traversal hops within KG paths, XAIRec learns user preferences from historical data, determines optimal reasoning paths, and enhances recommendation quality by prioritising products with higher relevance. A core innovation within XAIRec is the Product Prioritisation Score (PPS) algorithm, which ranks recommendations based on their user-specific relevance. Extensive evaluations across Amazon domains, including CDs & Vinyl, Beauty, Clothing, and Cellphones, demonstrate XAIRec’s strong performance, with significant improvements in key metrics such as NDCG (27.5% for CDs & Vinyl, 8.4% for Clothing, 5.8% for Cellphones, and 6.1% for Beauty), along with comparable gains in Recall, Hit Ratio, and Precision. These results underscore XAIRec’s advancement in developing explainable, user-centric recommender systems. Code Repository: https://github.com/Neeraj-Tiwary/PhD-ExplainableRecommendation |
| format | Article |
| id | doaj-art-7e51f38ae6464d948e6a2f7569d9a6a2 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-7e51f38ae6464d948e6a2f7569d9a6a22025-08-20T04:02:41ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137612410.1007/s44443-025-00173-5Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learningNeeraj Tiwary0Shahrul Azman Mohd Noah1Fariza Fauzi2Center for Artificial Intelligence Technology, Faculty of Information Science & Technology, Universiti Kebangsaan MalaysiaCenter for Artificial Intelligence Technology, Faculty of Information Science & Technology, Universiti Kebangsaan MalaysiaCenter for Cyber Security, Faculty of Information Science & Technology, Universiti Kebangsaan MalaysiaAbstract Explainable Recommender Systems (XRSs) assist users in decision-making by offering precise and interpretable recommendations. However, most existing XRSs tend to prioritise either accuracy or explainability in isolation, with limited attention to aligning recommendations with individual user preferences while providing well-substantiated justifications. To address this gap, we propose the Explainable Artificial Intelligence-driven Recommender System (XAIRec), which incorporates explainability mechanisms into knowledge graphs (KGs) and leverages knowledge graph embeddings (KGEs) alongside reinforcement learning (RL) to enhance both transparency and interpretability. By applying optimisation techniques that consider rewards, probabilities, information values, and affinity scores while minimising traversal hops within KG paths, XAIRec learns user preferences from historical data, determines optimal reasoning paths, and enhances recommendation quality by prioritising products with higher relevance. A core innovation within XAIRec is the Product Prioritisation Score (PPS) algorithm, which ranks recommendations based on their user-specific relevance. Extensive evaluations across Amazon domains, including CDs & Vinyl, Beauty, Clothing, and Cellphones, demonstrate XAIRec’s strong performance, with significant improvements in key metrics such as NDCG (27.5% for CDs & Vinyl, 8.4% for Clothing, 5.8% for Cellphones, and 6.1% for Beauty), along with comparable gains in Recall, Hit Ratio, and Precision. These results underscore XAIRec’s advancement in developing explainable, user-centric recommender systems. Code Repository: https://github.com/Neeraj-Tiwary/PhD-ExplainableRecommendationhttps://doi.org/10.1007/s44443-025-00173-5Explainable recommender systemKnowledge graphKnowledge graph embeddingReinforcement learningExplainable artificial intelligence |
| spellingShingle | Neeraj Tiwary Shahrul Azman Mohd Noah Fariza Fauzi Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning Journal of King Saud University: Computer and Information Sciences Explainable recommender system Knowledge graph Knowledge graph embedding Reinforcement learning Explainable artificial intelligence |
| title | Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning |
| title_full | Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning |
| title_fullStr | Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning |
| title_full_unstemmed | Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning |
| title_short | Prioritising explainable AI-driven recommendations with knowledge graphs and reinforcement learning |
| title_sort | prioritising explainable ai driven recommendations with knowledge graphs and reinforcement learning |
| topic | Explainable recommender system Knowledge graph Knowledge graph embedding Reinforcement learning Explainable artificial intelligence |
| url | https://doi.org/10.1007/s44443-025-00173-5 |
| work_keys_str_mv | AT neerajtiwary prioritisingexplainableaidrivenrecommendationswithknowledgegraphsandreinforcementlearning AT shahrulazmanmohdnoah prioritisingexplainableaidrivenrecommendationswithknowledgegraphsandreinforcementlearning AT farizafauzi prioritisingexplainableaidrivenrecommendationswithknowledgegraphsandreinforcementlearning |