E-Commerce Suggestion Engine: A Machine Learning based Dual Filtering Approach

An e-commerce product recommendation system that offers tailored product suggestions based on customer preferences is proposed. New users with limited browsing experience in online shopping often encounter challenges in selecting products that meet their needs. To address such challenges, the proposed methodology system helps users find products aligned with their preferences, improving the shopping experience and facilitating personalized product discovery. The system utilizes a combination of content-based filtering and collaborative filtering methodologies. Content-based filtering recommends products by matching keywords in product descriptions to user preferences, while collaborative filtering identifies similarities among customers and products to suggest relevant items. Advanced techniques, such as cosine similarity in collaborative filtering and Term Frequency-Inverse Document Frequency (TF-IDF) with Cosine Similarity in content-based filtering, are employed to enhance accuracy and efficiency. TF-IDF analyzes the frequency of a word in a product description and its uniqueness across all descriptions to determine its importance. A Cosine Similarity calculates the degree of similarity between user preferences and product descriptions, enabling the system to rank products precisely and recommend the most relevant options. The system effectively addresses the challenge of recommending products for new users and items with limited interaction data, delivering highly accurate and optimized suggestions. Features such as brand visualization using product ratings to highlights the top-rated products based on brands. By integrating advanced machine learning techniques, the system ensures scalability, adaptability, and enhanced personalization, making it a valuable asset for modern e-commerce platforms.