A recommender system is a popular tool used by companies to increase customer satisfaction and to increase revenue. Collaborative filtering and content-based filtering are the two most common approaches when implementing a recommender system, where the former provides recommendations based on user behaviour, and the latter uses the characteristics of the items that are recommended. The aim of the study was to develop and compare different recommender system approaches, for both book and author recommendations and their ability to predict user ratings of an e-book application. The evaluation of the models was done by measuring root mean square error (RMSE) and mean absolute error (MAE). Two pure models were developed, one based on collaborative filtering and one based on content-based filtering. Also, three different hybrid models using a combination of the two pure approaches were developed and compared to the pure models. The study also explored how aggregation of book data to author level could be used to implement an author recommender system. The results showed that the aggregated author data was more difficult to predict. However, it was difficult to draw any conclusions of the performance on author data due to the data aggregation. Although it was clear that it was possible to derive author recommendations based on data from books. The study also showed that the collaborative filtering model performed better than the content-based filtering model according to RMSE but not according to MAE. The lowest RMSE and MAE, however, were achieved by combining the two approaches in a hybrid model.