Research On Learning Resource Group Recommendation Model Based On Collaborative Filtering And Its Application

In the highly development of network and information techniques, the massive open online and internet (MOOCs) forms of learning has been widely concerned. There are characteristics of the large-scale, open courses and online learning in MOOCs which are widely caught on the learners and rapidly turned into an important form of the modern education system. In their development, due to the increased number of e-learning resources which has been flooded in the network make the online learners hard to find the useful resource. According to the problem of the "information overload", people need an information filtering technology to solve it.The collaborative filtering(CF) which has been successfully applied to many aspects of e-commerce is an effective information filtering technology. According to its knowledge discovery technology, the collaborative filtering can effectively solve the problem of "information overload" in learning process. The principle and process of recommendation is based on the history of users information. Then, it find the neighbor set which is similar to the formation of target user and use the neighbor set to recommend for the target user. However, the traditional collaborative filtering recommendation which is applied to learning resource is faced questions of the large number of learning resource, data sparseness, similarity precision and so on. The recommendation which is based on individual can not suit for the group learning formation in MOOCs How to recommend accurately results which have common learning objectives to suit the learning process for the learners? This questions is generated the requirements which is recommended the learning resource to group.The research in this paper is aimed at designing a learning resources group recommendation model based on collaborative filtering and improving problem faced by the traditional collaborative filtering algorithms. It is satisfied to the needs of users recommendation in group learning.Work in this paper mainly includes:First, through the research of the existing literature it is analysed the relevant forms of learning groups, personalized collaborative filtering recommendation system theories and design a learning resources group recommendation model based on collaborative filtering.Second, to further study of group learning forms, learners measure, group learning, similarity measure and other issues is discussed.Third, compared with the personalized recommendation technology, the collaborative filtering is introduced in details. Aimed at problems in the CF algorithm is improved.Fourth, from the perspective of the recommendation by using the improved algorithm to group recommendation model based on collaborative filtering is evaluated and analysed.Finally, the learning resources group recommendation model based on collaborative filtering is built and evaluated the prototype.