A Practitioner’s Runbook for Building Scalable Recommendation Systems in Large-Scale E-Commerce Platforms

Abstract

Personalized recommendation systems sit at the heart of large-scale e-commerce platforms, directly shaping how users discover products, engage with content, and return over time. Delivering high-quality recommendations is not a secondary concern; it is a primary driver of retention, satisfaction, and sustained platform growth. Building these systems at scale demands careful coordination of several engineering components, from capturing recent user behavior and determining interest categories to constructing features for AI/ML inference pipelines and closing the loop through user interaction feedback integration, all while operating under strict latency and accuracy requirements. The strength of a recommendation system depends as much on how its components are architecturally organized as on the sophistication of the models running inside it. This article covers the end-to-end design of recommendation pipelines across four areas. First, context building draws on user interactions and trending signals to assemble the inputs the algorithm needs. Second, the online inferencing algorithm handles low-latency candidate retrieval, AI/ML model ranking, and post-processing feedback that feeds offline training. Third, scaling and reusability strategies allow generic pipelines to serve multiple pages, surfaces, and category-specific experiences through caching, unified interfaces, and deduplication. Fourth, broader implications are examined, including how well-designed systems build user trust, reduce operational overhead, and support healthy ecosystem diversity. Recommendation systems function as strategic infrastructure rather than isolated technical components. The architectural decisions made during their design shape day-to-day performance as well as the long-term resilience and competitive position of the platform.