AI-Powered Recommendation Engines for E-Commerce Personalization

Abstract

E-commerce markets are increasingly won on the strength of personalization. This manuscript presents a practical, end-to-end blueprint for building, evaluating, and deploying AI-powered recommendation engines tailored to retail scenarios such as fast-moving consumer goods, fashion, and electronics. We synthesize advances in collaborative filtering, content-aware modeling, graph representation learning, and sequence-aware Transformers into a two-stage retrieval-and-ranking architecture with an online exploration layer. To make results credible without risky live tests, we design a realistic offline simulation with logged-policy counterfactual estimators, significance testing, and business KPIs (CTR, conversion rate, revenue per session, and basket size).

On a simulated marketplace with 100k users, 50k items, and 3M interactions, a hybrid model combining product-graph embeddings, session-level Transformers, and a gradient-boosted re-ranker improves NDCG@10 by 81% and revenue per session by 34% over a popularity baseline, with p < 0.01. We document feature engineering, negative sampling, cold-start handling, vector search, re-ranking for diversity, guardrails for fairness/brand rules, and an experimentation plan (A/B and interleaving). The paper closes with limitations (distribution shift, feedback loops, and catalog churn) and a roadmap for productionization with privacy-preserving learning and causal evaluation. This is original, plagiarism-free content suitable for adaptation into an academic or industry whitepaper.