Improving an Hybrid Literary Book Recommendation System through Author Ranking
This work addresses book choice for readers and library users, but it is incremental as it builds on existing collaborative filtering methods.
The paper tackled the problem of book recommendation by proposing a hybrid system that combines two item-based collaborative filtering algorithms to predict books and authors, showing that author recommendations can improve overall book recommendations.
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.