Modern recommender systems draw on three primary data categories: an item catalog, a user catalog, and a history of user-item interactions. The item catalog stores both active and historical items, which matters because historical items help measure similarity between users who interacted with them in the past. The user catalog holds (often optional) attributes such as location, subscription status, and user bio. Together, these sources allow a recommender to build accurate, personalized outputs even before a user has accumulated a long interaction history. Teams integrating a recommender should plan data pipelines for all three categories from the start.