First Steps towards Implicit Feedback for Recommender Systems in Electronic Books

摘要

Currently, a variety of eBooks with some intelligent capabilities to store and read digital books have been developed. With the use of these devices is easier to interact with the content available on the Web. But in some way access to such content is limited due to data overload problems. Trying to resolve this problem have been developed some techniques for information retrieval, among which are the recommender systems. These systems attempt to measure the taste and interest of users for some content and provide information relating to your profile. Through the feedback process attempts to collect the information that a recommendation system needs to work; but often this process requires the direct intervention of users, so that sometimes it is tedious and uncomfortable for users. For what we believe is necessary for a recommender system should be able to capture and measure implicitly the interaction parameters of a user with content in an eBook. Considering this need, we present a series of parameters that can be measured implicitly and how they will measured in the feedback process so that a recommender system to be reliable in electronic books.