We demonstrate how user ratings can be accurately predicted from a set of tags assigned to a book on a social-networking site. Since a newly-published book is unlikely to have social-tags already assigned to it, we describe a probabilistic model for inferring the most probable tags from the text of the book. We evaluate the proposed approach on a newly-created corpus, involving 146 books and 1060 users. Our experiments demonstrate that the proposed approach is significantly better than a well-tuned collaborative filtering baseline for books with 10 or fewer ratings. We also show how predictions based on social-tags can be combined with the traditional collaborative-filtering methods to yield superior performance with any number of ratings.