@article{najafi_khoshnam_jahangiri_2015, 
  title={The Improvement of Electronic Learning's Recommender Systems Performance}, 
  abstractNote={Today, the virtual environment is becoming more and more widespread as far as the control and processing of information is almost impossible. Therefore, the need for a system that can overcome this matter is felt more than ever. The systems that suggest the best and most friendly cases from among huge numbers of different products and data, according to the specific characteristics of each user, are very popular. The recommender systems are intelligent systems in the internet which identify the interests and preferences of users and offer relevant information to them. This study aimed to introduce recommender systems, analyze their techniques in detail, study the role of these systems in e-Learning to improve the learning of users in a virtual learning environment, and enhance the quality of recommendations with involvement of users' information level in cooperative filtration algorithm to enhance the quality of users' learning. Introduction The electronic world is moving towards saturation of information. During the past decade, large volumes of data are stored on information servers and databases. Today, the amount of available data doubles every five years. Considering large variety of information, the access to suitable data seems necessary for proper decision making. In fact, along with increased options, the volume of information that must be processed to achieve the target and the amount of time and energy to reach the final data increases dramatically. In such environments, the systems with the ability to identify and update the interests and priorities of users and with the ability to index and store the information in a searchable manner are needed to predict and identify users requirements and direct them to find appropriate materials. To date, several methods have been proposed to solve the problem of accumulation of information. One of these methods is the use of search engines. But so}, 
  author={Najafi and Khoshnam and Jahangiri}, 
  year={2015}
  }