In this paper, we perform a new strategy for recommender systems in online entertainment platforms. As a case of study, we analyzed the reading preferences based on users of Goodreads, a social network for readers, to classify the books depending on their associated with variables as average rating, rating count, and text review count. Multivariate techniques cluster analysis and benchmarking for comparison of predictive models were used. Graphs and data are presented, allowing optimal evaluation of the number of clusters and the precision of models. Finally, we show the existence of groups of elements that can be forgotten by traditional recommendation systems, due to their low visualization on the platform. It is proposed to use promotional strategies to highlight these high-quality articles but with little visibility. All in all, consider the classification of books that predictive models can offer, it can favor the authors, readers, and investors of Goodreads, by the retention and attraction of users.