Mission: Build a Data-Driven Recommendation System

Data-driven recommendation engines transform user behavior data into personalized experiences that measurably increase engagement and revenue. This mission deploys your AI squad to build a recommendation system using collaborative filtering for user-similarity signals, content-based similarity for item attributes, and real-time user signals for session-level personalization. Forge builds the recommendation algorithm starting with as little as 1,000 user interactions, implements A/B testing for comparing algorithmic approaches, and creates a performant recommendation API. The squad handles the cold-start problem by combining popularity-based, content-based, and demographic signals for new users and items until enough personalized data accumulates. ShipSquad recommendation engines deliver measurable business impact: 20-40% improvements in click-through rates and 10-30% increases in revenue per session are typical for well-tuned implementations. We build performance analytics that show exactly how recommendations drive engagement so you can continuously optimize. The mission delivers in 4-6 weeks with a production-ready recommendation engine, A/B testing infrastructure, and the analytics to prove its value.