Context-Aware Recommendation Systems: Time, Location, Device, Intent, and Situational Preference Modeling

Context-Aware Recommendation Systems is a systems-oriented engineering guide to building recommenders that adapt to time, location, device, and situational intent - not just static user preferences. This book walks through the full context-aware recommendation stack: what actually counts as context, how to define feature semantics that stay consistent between offline training and online serving, and how to model contextual user intent. It then covers the core modeling techniques - context-aware matrix factorization, factorization machines, tensor approaches, and contextual bandits - before moving into device, location, and time-specific effects. Inside, you'll learn how to: - Define context features with event-time semantics that hold up between training and serving - Model contextual user intent, mission type, and situational preference shifts - Apply context-aware matrix factorization, factorization machines, and contextual bandits - Handle device, screen, and network effects on recommendation quality - Build privacy-aware context pipelines with proper minimization, consent, and retention - Detect and respond to context drift as devices, mobility patterns, and policies change >Written for machine learning engineers and recommender systems practitioners, each chapter ends with a practical "Context Audit" exercise and a check you can apply directly to your own production system.