Final answer:
kNN predictive models may not be useful for recommending a new song or for a new user due to the cold start problem. Alternative recommendation methods using metadata or content-based filtering are usually employed until sufficient data is available for collaborative filtering techniques.
Step-by-step explanation:
The question asks whether kNN predictive models (k-nearest neighbors) could be useful for generating recommendations for a new song that has not yet been rated by users, or for a new user with no rating history. kNN models are a type of collaborative filtering algorithms used in recommender systems, which suggest items based on the preferences of similar users or items.
However, these models face a challenge known as the cold start problem when dealing with new items (like a song) or new users that have no previous interaction data. In such cases, kNN may not be effective due to the lack of data to identify 'neighbors'. Alternative approaches like content-based filtering or the use of metadata (e.g., genre, artist, release date for songs) might be needed to provide recommendations until enough data is collected.