Book recommender systems can help promote the practice of reading for
pleasure, which has been declining in recent years. One factor that
influences reading preferences is writing style; we propose a system that
recommends books after learning their authors' writing style.To our
knowledge, this is the first work that applies the information learned by
an author-identification model to book recommendations. We also explore
more than a hundred linguistic features that may play a role in generating
high-quality book recommendations. We evaluated the system according to a
top-k recommendation scenario. Our system gives better accuracy when
compared with many state-of-the-art methods. We also conducted a
qualitative analysis by checking if similar books/authors were annotated
similarly by experts.