International journal of Computer Science & Network Solutions Jan.2015-Volume 3.No.1 http://www.ijcsns.com ISSN 2345-3397 21 Social Networks based e-Learning Systems via Review of Recommender Systems Techniques A. A. Salama, M.M.Eisa, S.A.EL-Hafeez, M. M. Lotfy Department of Mathematics and Computer Science, Faculty of Science, Port Said University, Egypt Department of Computer Science, Higher Institute of Management and Computers, Port Said University, Egypt Abstract e-Learning has turned to be a necessity for everyone, as it enables continuous and life-long education. Learners are social by nature. They want to connect to othersand share the same interests. Online communities are important to help and encourage learners to continue education. Learners through social capabilities can share different experiences.Social networks are cornerstone for e-Learning. However, alternatives are many. Learners might get lost in the tremendous learning resources that are available. It is the role of recommender systems to help learners find their own way through e-Learning. We present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Keywords: Social Networks, e-Learning ,Techniques I. Introduction The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. A vast array of systems exist which employ users’ stored profile data, identifying matches for collaboration. Social interaction within an online framework can help university students share experiences and collaborate on relevant topics. As such, social networks can act as a pedagogical agent, for example, with problem-based learning [1].Social networking websites are virtual communities which allow people to connect and interact with each other on a particular subject or to just ‘‘hang out” together online. Membership of online social networks has recently exploded at an exponential rate [2]. Recommender systems cover an important field within collaborative services that are developed in the Web 2.0 environment and enable user-generated opinions to be exploited in a