Looking at how to build a recommendation system involves gathering datasets, selecting algorithms, training models, and integrating real-time inference. Popular techniques include collaborative filtering, content-based filtering, and hybrid models. For example, eCommerce platforms use recommendation engines to boost conversions by 35% through personalized product suggestions. Key differentiators include scalable data pipelines, user behavior analytics, and continuous model retraining. High-performing systems incorporate A/B testing and feedback loops. Companies like Debut Infotech develop recommendation architectures that process large volumes of interaction data and deliver precise, context-aware recommendations tailored to user intent.