Abstract: Cross-Domain Recommendation Systems (CDRS) enhance traditional recommendation models by transferring knowledge across different domains, thereby improving the personalization of suggested content. The integration of Explainable AI (XAI) ensures transparency in recommendation systems, addressing concerns regarding trust and interpretability. Additionally, sentiment analysis is crucial in refining recommendations by capturing the emotions embedded in user reviews and feedback. This paper explores the concept of a sentiment-aware and explainable AI-based cross-domain recommendation system by discussing key methodologies, challenges, and potential advancements in the field. The research highlights how a hybrid recommendation model can optimize user satisfaction by balancing accuracy, interpretability, and personalized content delivery.
Keywords: Cross-Domain Recommendation, Explainable AI, Sentiment Analysis, Hybrid Recommendation Model, User Trust.
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DOI:
10.17148/IJARCCE.2025.14385
