Recommender Systems are software techniques that are being widely used in many applications to suggest products, services, and items to potential users. The main purpose of Recommender Systems is to provide meaningful recommendations about the items or products to a collection of users for their interested items. There are two popular approaches in recommendation: user-based and item-based collaborative filtering. The difference between them is that user-based takes users’ behaviors and item-based takes items’ rating values. The purpose of this paper is to present a recommender system that provides meaningful recommended mobile phone applications to mobile phone users which are relative to their needs or targets. This system emphasizes mainly on item-based collaborative filtering method that bases on rating values of the items because the computational complexity of user-based recommendation grows linearly with the number of users. By using this system, mobile phone applications users can obtain optimized suggestions without their waste of time and effort.