A Hybrid Book Recommender System Based on Table of Contents (ToC) and Association Rule Mining
Recommender systems are used to access appropriate items and information by personalized suggestions based on user previous preferences and their likes dislikes. These systems are used in different domains including products, videos, images, articles, news and books. Several recommender systems have been designed for recommending books. However, the available book recommenders face several issues in making relevant book recommendations because most of these do not take into account the book contents at deeper level and process only the mere descriptions about books on web pages along with metadata and other rating information. In order to cope with this issue, we present a hybrid book recommender that recommends books by using book table of contents (TOC) along with association rule mining and opinions of similar users.
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