Operation Patterns in Recommendation Systems: Limitations, Functionalities and Performance in the Digital Environment

The main purpose of this article is to analyze different recommendation systems and examine how they are used within digital environments to establish classifications for books. Following a bibliographical review of recommendation systems, the performance of a number of book recommendation systems is tested. The systems tested are grouped according to whether recommendations are done by specialists, are based on social networking or use more complex statistical stylometry to help each reader find the reading materials best suited to them. Results indicate that progress in technology implementation is favoring the findability of books by combining the strengths of the various systems. The principal social implication of this research is that recommendation systems enable the reader's optimized use of books, as well as allow the development of content appropriation systems. Concerning to originality and value is important to emphasize there is no previous known work establishing the taxonomy proposed in this paper.