%0 Conference Proceedings %T Authorship Identification for Literary Book Recommendations %A Alharthi, Haifa %A Inkpen, Diana %A Szpakowicz, Stan %Y Bender, Emily M. %Y Derczynski, Leon %Y Isabelle, Pierre %S Proceedings of the 27th International Conference on Computational Linguistics %D 2018 %8 August %I Association for Computational Linguistics %C Santa Fe, New Mexico, USA %F alharthi-etal-2018-authorship %X 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’ style. To our knowledge, this is the first work that applies the information learned by an author-identification model to 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. %U https://aclanthology.org/C18-1033 %P 390-400