Personalized E-commerce Intelligence Engine
An intelligent recommender engine delivering hyper-personalized product suggestions based on user behavior, product metadata, and hybrid ML algorithms.
Initially conceived as a startup MVP, this project blends collaborative filtering + content-based intelligence to boost user engagement and solve the cold start problem.
Finds user similarity via cosine distance to recommend products.
Analyzes tags, categories & brands to suggest similar items.
Bootstraps personalization for new users with onboarding inputs.
Weighted combination of CF + CBF ensures accuracy & diversity.
Outperformed rule-based systems with a 40% improvement in recommendation relevance, demonstrating strong viability as a scalable product.