It is obvious that, for collaborative filtering to work, we need to have historical data about user preferences. For a new user, we may not have any data, so our user similarity algorithm will be based on assumptions that may not be accurate. For content-based recommendations, we may not have the details about the new items right away. This requirement of having data about items and users to generate high-quality recommendations is called the cold start problem.