Personalized Recommender System for E-Commerce

In the fast-changing e-commerce world, personalization with regard to increasing user engagement and driving sales is becoming essential. As products and interactions grow voluminous, traditional recommender systems suffer from key challenges related to the cold-start problem, data sparsity, and offering static and repetitive recommendations that do not adjust for preferences' changes. These lead to irritation by way of inappropriate suggestion and inefficient shopping experience. In the midst of these challenges, this paper proposes a hybrid form of recommender system, in that it combines collaboration filtering with contentbased filtering, CNNs in analyzing images, and it also uses VADER, as well as Large Language Models, to determine the sentiment. Instead of analyzing only patterns in user behavior for collaborative filtering, this system suggests products liked by similar users. For content-based filtering, it outlines product attributes that will help suggest items about which the user has previously interacted. By integrating various methods into one, this system will always guarantee a higher accuracy level and diversity of the recommendations. Apart from that, CNN-based image analysis can further improve personalization with visually attractive product recommendations based on users' preferences, particularly in visually driven categories such as fashion and home decoration. This also sets up a sophisticated filtering of the recommendations based on sentiment analysis and favours products for which reviews are positive so that users get only high-quality suggestions. This is a hybrid method that overcomes two of the significant problems of traditional systems, namely the cold-start problem and data redundancy, through dynamic, context-aware product recommendations. It enhances user satisfaction, improves sales conversion rates, and provides a more entertaining shopping experience.