AI-Powered Hybrid Recommendation System for E-Commerce

Personalization is becoming crucial in the rapidly evolving world of e-commerce in order to boost user engagement and sales. The cold-start problem, data sparsity, and providing static and repetitive recommendations that don’t adapt to changing tastes are the main issues that traditional recommender systems face as the volume of items and interactions increases. Inappropriate suggestions and ineffective shopping experiences cause annoyance. In the midst of these challenges, this paper proposes a hybrid form of recommender system that combines collaborative filtering with content-based filtering, utilizing the Llama Index, CNNs in image search functionality, and VADER to determine sentiment. Instead of analyzing only patterns in user behavior for collaborative filtering, this system suggests products liked by similar users. It lists product attributes for content-based filtering, which can help recommend products the user has already interacted with. This strategy will ensure a greater level of accuracy and diversity in the recommendations by combining multiple approaches into one. In addition, the CNN-based image searching feature can enhance personalization by suggesting visually appealing products according to user preferences, especially in visually-driven categories like fashion and home decor. To ensure that consumers receive only top-notch recommendations, this system also implements a sophisticated sentiment analysis-based filtering system that favors products with favorable ratings. One of the major additions to this system is a smart chatbot that enables real-time interaction, product availability inquiries, and immediate responses based on a current database. This chatbot not only simplifies the shopping process but also dynamically adjusts to user questions, further enhancing recommendation accuracy. It makes shopping more enjoyable, increases customer satisfaction, and boosts sales conversion rates.