The cold start problem in information retrieval (IR) refers to the challenge of providing effective search results when there is limited data available. This typically occurs when a new system is deployed, or when a new user or item is introduced into a system with little to no historical interaction or feedback.
For instance, in a recommendation system, when a user has no previous activity or when a new item is added, the system struggles to provide accurate results because it lacks sufficient data to predict preferences. Solutions include using content-based methods, where recommendations are based on the characteristics of items or users, and collaborative filtering, which leverages the preferences of similar users.
Another approach to addressing the cold start problem is to rely on external data sources, such as demographic information or social media activity, to fill in the gaps and provide more personalized recommendations.