Published Paper Details:

Recommender systems have become pivotal in the digital era, enhancing customer experiences by providing personalized suggestions across different domains such as e commerce, entertainment, and social media. This paper presents a comprebensive literature survey and proposes a novel hybrid framework that integrates image based data with advanced mathemnatical models improve recommendation accuracy and user engagement. We develop an image enhanced matrix factorization method and a visually aware neural collaborative fltering model, and we evaluate both on standard benchmarks, achieving a 15 % improvement in F1 score over leading baselines. Detailed mathematical formulations of each algorithm are provided, along with a thorough comparison of efficiency and scal ealability, The results demonstrate that ncopor visual features significantly enbances the relevance and recommendations, ultimately boosting customer satisfaction.