Collaborative Filtering
A common approach for executing product recommendations using machine learning, collaborative filtering looks at the collective set of preferences across users and items to learn from users that have similar behavior patterns. Recommendation systems collect both explicit data actively provided by users (such as numeric rating) and implicit data inferred by the system based on a user’s behavior (such as a preference for a certain product after viewing similar ones in the past). These massive datasets allow systems to craft predictions and serve relevant product recommendations tailored to users’ individual affinities and shopping behaviors.