This document presents a recommendation system for books that uses item-based and user-based collaborative filtering techniques. It discusses collecting rating data from many users, calculating item and user similarities, finding the k-nearest neighbors, making predictions of user ratings, and evaluating the recommendation performance using metrics like MAE and RMSE. The system aims to recommend the top-N most relevant books to a user based on these collaborative filtering approaches.