Cold Start and Learning Resource Recommendation Mechanism Based on Opportunistic Network in the Context of Campus Collaborative Learning

Abstract

The mainstream cold start scheme in social network mainly deals with the problems of information overload and the accuracy and efficiency of recommendation. However, the problem of information overload is quite different from the problem of information transmission delay caused by insufficient contact of nodes in the mobile Opportunistic network. And in the campus collaborative learning environment, learner nodes often have a lack of awareness of their own needs of learning resources and lack of search ability for learning resources, in order to solve the above problems, this paper for the mobile social network cold start stage definition and stage division, On this basis, the paper provides solutions to the file transfer strategies in the cold start-up stage and the community operation stage of the nodes respectively, And according to the high degree of activity nodes can often be contact more information, the higher intimacy between nodes means that the nodes are more familiar and higher transmission success rate characteristics, In this paper, a learning resource recommendation mechanism based on node activity and social intimacy is proposed, and the algorithm has been tested and verified to have high accuracy for the recommendation mechanism based on message attributes.