Improving customer's profile in recommender systems using time context and group preferences

By the expanse of internet stores and products, recommender systems have emerged to increase store attractiveness and develop online customers. Recommender systems are systems which help customers to find product that they want. These systems recommend product to individual customer according to their preferences and interests. Recommender systems use several ways such as collaborative filtering and content-based filtering to create recommendation. In this study we proposed a recommender system based on collaborative filtering. In proposed model we endeavored to improve the customer profile in collaborative systems to enhance the recommender system efficiency. We do this improvement using time context and group preferences.