Resource recommendation system based on similar learners exploitation

A novel E-learning resource collaborative recommendation mechanism based on multi-agent was proposed to solve the big challenge to provide personalized learning resource to large-scale and distributed E-learners.A learning status evaluation vector was introduced to model the E-learners' behaviors, and help to find and reorganize the learners' share of similar learning status into smaller communities based on the interaction between group agents.Furthermore a collaborative communication and learning resource recommendation platform was developed to enable the learner to share personalized recommended resources.Experimental results showed that this algorithm has higher discovery accuracy of similar E-learner and construction efficiency of community.Based on the learning resource recommendation and sharing among E-learners in the common community,this system is proved to enhance the learning effect and scores.