Implicit feedback techniques on recommender systems applied to electronic books

The goal of this research is to define and capture a series of parameters that allowed us to perform a comparative analysis and find correlations between explicit and implicit feedback on recommender systems. Most of these systems require explicit actions from the users, such as rating, and commenting. In the context of electronic books this interaction may alter the patterns of reading and understanding of the users, as they are asked to stop reading and rate the content. By simulating the behavior of an electronic book reader we have improved the feedback process, by implicitly capturing, measuring, and classifying the information needed to discover user interests. In these times of information overload, we can now develop recommender systems that are mostly based on the user’s behavior, by relying on the obtained results.

► The more time content is displayed by a user, the more he likes it. ► When a user accesses multiple times a category it is because he likes the contents. ► No strong relation between the display of items of a content and its ratings.