Improving a hybrid literary book recommendation system through author ranking
Literary reading is an important activity for individuals and can be a long term commitment, making book choice an important task for book lovers and public library users. In this paper, we present a hybrid recommendation system to help readers decide which book to read next. We study book and author recommendations in a hybrid recommendation setting and test our algorithm on the LitRec data set. Our hybrid method combines two item-based collaborative filtering algorithms to predict books and authors that the user will like. Author predictions are expanded into a booklist that is subsequently aggregated with the former book predictions. Finally, the resulting booklist is used to yield the top-n book recommendations. By means of various experiments, we demonstrate that author recommendation can improve overall book recommendation.
Comments by delegates: ::: "A very interesting and stimulating presentation" - Prof. Peter G. Underwood (Professor of Librarianship, Univ. of Cape Town); ::: "A benchmarking library and a benchmarking presentation" - Dr Brian Brink (Univ. of Limpopo)