CEARS: A MODULAR AND INTERPRETABLE COLLABORATIVE FILTERING FRAMEWORK FOR E-COMMERCE RECOMMENDATION

The exponential growth of e-commerce platforms has resulted in an overwhelming volume of products, making it increasingly difficult for users to discover relevant items efficiently. Intelligent Recommendation Systems (IRS) have therefore become a cornerstone of modern online retail, enabling personalized shopping experiences and improving business revenue. Despite significant advances in machine learning and deep learning based recommender systems, several persistent challenges remain, including data sparsity, cold-start problems, lack of transparency, and limited adaptability to dynamic user behaviour. This paper introduces CEARS (Collaborative Explainable Adaptive Recommendation System), a modular and interpretable recommendation framework specifically designed for large-scale e-commerce environments. Unlike conventional black-box recommender systems, CEARS integrates hybrid collaborative filtering, sentiment-aware learning, explainable artificial intelligence, and adaptive ranking strategies into a unified architecture. The framework leverages user interactions, product metadata, and customer reviews to produce accurate and transparent recommendations. Experimental evaluation demonstrates that CEARS improves precision, recall, ranking quality, and click-through rates while enhancing user trust through interpretable recommendations. The proposed work contributes to the development of trustworthy and scalable recommender systems for next-generation digital commerce.