1 research outputs found
Improving an Hybrid Literary Book Recommendation System through Author Ranking
Literary reading is an important activity for individuals and choosing to
read a book can be a long time commitment, making book choice an important task
for book lovers and public library users. In this paper we present an hybrid
recommendation system to help readers decide which book to read next. We study
book and author recommendation in an hybrid recommendation setting and test our
approach in the LitRec data set. Our hybrid book recommendation approach
purposed combines two item-based collaborative filtering algorithms to predict
books and authors that the user will like. Author predictions are expanded in
to a book list that is subsequently aggregated with the former list generated
through the initial collaborative recommender. Finally, the resulting book list
is used to yield the top-n book recommendations. By means of various
experiments, we demonstrate that author recommendation can improve overall book
recommendation.Comment: Submitted to JCDL 201