Recommender systems naively built on predictive models can suffer from feedback loops, where the learning algorithm fails to account for the fact that current purchase habits are heavily influenced by the recommendation algorithm itself. This creates a cycle where a system preferentially pushes an item, leading to greater purchases, which the model then interprets as the item being inherently better, causing it to be recommended even more frequently.