The Internet revolution changed the world and made it as a small village, since everyone can contact people anywhere in the world. This easy communication facilitates selling and buying through the Internet which is called e-commerce. When e-commerce began to grow, problems appeared, one of them is how to buy something from a huge category, i.e. when a customer wants to buy something from internet markets, he will be confused what to choose and from where, because of the various items and enormous sites. People handle this information overload through their own effort, the effort of others and some blind luck. First of all, most items and information are removed from the stream simply because they are either inaccessible or invisible to the user. Second, a large amount of filtering is done for us. Newspaper editors select what articles their readers want to read. Bookstores decide what books to carry. However with the dawn of the electronic information age, this barrier will become less and less a factor. Finally, we rely on friends and other people whose judgement we trust to make recommendations to us. A technology is needed to help people wade through all the information to find the items they really want and need, and to rid them of the things they do not want to be bothered with. Recommender systems are the new technology that assist and augment the recommendation process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In this work, a recommender system is built that uses different recommendation methods.