Which method can be employed to address the cold-start problem in collaborative filtering?

The cold-start problem in collaborative filtering typically refers to the challenge of making recommendations when there is insufficient data available for new users or new items. Addressing this issue requires strategies that enable a system to effectively generate recommendations without relying heavily on historical interaction data.

Asking users for their basic preferences is a direct approach to gathering initial user data. By collecting information such as user preferences, interests, or ratings on a small set of items, the system can start to build a basic profile for the new user. This data serves as a foundation for generating recommendations tailored to the user's specific tastes and needs, even in the absence of collaborative filtering data derived from interactions with similar users.

This method is particularly beneficial since it allows for immediate engagement with the user, ensuring that the recommendation system can provide personalized suggestions right from the start, rather than waiting to accumulate interaction data, which could take significant time.

In contrast to this option, using historical data from similar users necessitates that some data already be available, which is often not the case in cold-start scenarios. Implementing deep learning for predictions, while powerful, typically requires a substantial amount of data to train effectively, which again works against situations with cold-start conditions. Similarly, using demographic information does provide some context but may