Recommender systems appear among other reasons with the purpose to improve web information overload and ease information recovery. This kind of systems aid users to find contents in a non-difficult way and with minimal effort. Even though, a great number of these systems performance requires contents to be explicitly rated in order to determine user’s interest. When interacting with electronic books this performance may alter users reading and understanding patterns as they are asked to stop reading and rate the content. Therefore, the analysis of user behavior, preferences and reading background can be considered suitable for a recommender system to build collective web knowledge in a collaborative learning context. This way, recommender system can assist users in finding contents of their interest without explicit rating based on previous constructed knowledge. The goal of this research is to propose an architecture to build a content recommendation platform based on eBook reading user behavior, allowing users to learn about the digital content collaboratively. This platform is formed by web readers’ community that aids members in finding contents of their interest in an automatic way and with minimal effort.