Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Hybrid web recommender systems
The adaptive web
The impact of author ranking in a library catalogue
Proceedings of the 4th ACM workshop on Online books, complementary social media and crowdsourcing
Stylometric relevance-feedback towards a hybrid book recommendation algorithm
Proceedings of the fifth ACM workshop on Research advances in large digital book repositories and complementary media
Book recommender prototype based on author's writing style
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Understanding temporal dynamics of ratings in the book recommendation scenario
Proceedings of the 2013 International Conference on Information Systems and Design of Communication
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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.