Recommendation systems are the systems thathelp users to select suitable items from a large collection of items based on their tastes and interests. Such systems have become one of the most powerful tools in electronic commerce and social websites. Nonetheless , using these systems in e-commerce websites faces many drawbacks such as: cold start-up, scalability and sparsity. In this paper, we present a solution to cold-start-up problem, and compare between many association rule algorithms to select the most suitable one to solve the scalability and sparsity problems.