Abstract: Recommendation systems are important business applications with significant economic impact. In recent years, a large number of algorithms have been proposed for recommendation systems. We evaluate a wide range of recommendation algorithms. These algorithms include the popular user-based, simple search, collaborative filtering and item-based filtering algorithms. We have also devised a method to form clusters through split inversions.