E-Commerce Product Recommendation System

E-commerce platforms present customers with large and frequently changing product catalogues. Finding relevant products can requiresubstantial browsing, and generic rankings may not reflect an individual shopper’s interests, current needs, or budget. This paper proposesan e-commerce product recommendation system that combines product-content similarity with behavioural signals to create personalized,context-aware product suggestions. The design includes catalogue preparation, interaction-event processing, candidate generation, ranking,availability filtering, and feedback collection. A hybrid approach is proposed because content-based recommendations can serve new or lesspopular products, while collaborative signals can identify patterns across users with similar preferences. The system also includes practicalsafeguards such as privacy-aware data handling, diversity controls, explanations, and monitoring for popularity bias. A prototype could bedeveloped using a web interface, an API service, a product database, and a recommendation module. Evaluation is planned using rankingmetrics and usability measures; no experimental performance is claimed in this conceptual paper. The proposed design aims to improveproduct discovery while keeping recommendations understandable and under customer control.