Electronic Books Recommender System Based On Implicit Feedback Mechanism

In recent years recommender systems (RSs) has gained popularity to solve the problem of web information overload and redundancy. Recommendation system helps users in finding the contents of their interest with minimum efforts. Even though most of the systems use explicit rating to recommend the content of users interest. When reading the electronic books performance of user gets affected because each time user has to stop reading and rate the contents explicitly. To overcome such a problem this paper considers user behavior, preferences and reading background while building recommender system by using collaborative learning implicit approach. In this way recommender system can help users in finding contents of their interest by using implicit rating based on the previous knowledge available. The main goal of this paper is to design and implement architecture that implements a recommender system for electronic books which is based on implicit actions performed by users on books.