@InProceedings{alharthi-inkpen-szpakowicz:2018:C18-1,
  author    = {Alharthi, Haifa  and  Inkpen, Diana  and  Szpakowicz, Stan},
  title     = {Authorship Identification for Literary Book Recommendations},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {390--400},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/C18-1033}
}