Document Type



Recommender systems try to recommend articles of potential interest to a user with respect to the user's individual preferences. Such recommender systems are the focus of current interest in part because of their importance for e-business. Collaborative filtering is the most promising technique in recommender systems. It provides personalized recommendations according to user's preferences. But one of the problems of Collaborative filtering is cold-start. Here, we provide a novel approach for solving this problem through the attributes of items in order to recommend articles to more people for improving e-business.