I'm analyzing a set of news articles and user libraries. User library is the set of news articles shared by one user. Obviously, the rating is 1 (the article is in user's library) and 0, otherwise. I hypothetically present every user with M articles sorted by their predicted rating and evaluate based on which of these articles were actually in the library. So, my only evaluation metric is
recall@M = # of articles the user shared in top M / total # of articles the user shared
Using precision doesn't make sense as zero ratings are uncertain (a user is not interested in the article or is just unaware of it). If I can't afford the user studies (explicit user feedback on recommendations), what are other evaluation metrics can I use?