Jayabhaduri RadhakrishnanH. Naga R. Guna VardhanAkey SungheethaK. Dinesh Kumar ReddyK. Danush KumarB. Charan Reddy2025-08-042025-08-042025-04-1397983503554689798350355475https://doi.org/10.1109/ITIKD63574.2025.11004930https://gnanaganga.alliance.edu.in/handle/123456789/8431This paper presents an innovative approach to anime recommendation systems by integrating multi-modal deep learning with explainable AI techniques. We propose a novel framework that combines visual features, textual content, and user interaction data to create more accurate and interpretable recommendations. Our system addresses key challenges in ex-isting recommendation systems, including the cold-start problem and limited content understanding, through a hybrid architecture that leverages BERT-based natural language processing and convolutional neural networks for visual analysis. Experimental results demonstrate a 27% improvement in recommendation accuracy compared to traditional methods, while providing transparent explanations for recommendations through attention visualization.enAnime RecommendationDeep LearningExplain-Able AIMulti-Modal AnalysisBERTCollaborative FilteringContent-Based Filteringproceedings-article