A Personalized Recommendation System Of Educational Resources Based On Knowledge Interest Profile

With the development of education informatization construction in our country, scattered education resources are collected to be use, network education received the recognition from more and more people. However, with the increasing number of education resources, information overload phenomenon is becoming more and more obvious, increasing the difficulty of looking for education resources. How to quickly get resources that user really need and how to improve the learning efficiency are the problems need to be solve. Using the recommended system is a good way to solve this problem, it can show different resources for different people, and the resources are what they most interested in. how to obtain the resources characteristics? How to dig and update user interest? How to better use user interest for recommended? These three questions are the core of the whole education resources recommendation system.According to this situation, this paper proposes a personalized recommendation systemusing collaborative filtering model based on knowledge Point of education resources.At first, this article describes recommendation system theory and its advantages in detail, and then analyze several personalized recommendation technology and the user model theory in popular, compare their advantage and dis advantages. Then in view the characteristics of the existing education resources platform of education resources and their user’s behavior. Set up education resources of knowledge point model and user interest model of knowledge point, and design the user knowledge interest value transfer method to simulate the user’s learning process. Basis on the models established, using the thought of collaborative filtering calculate the predicted interest value of user knowledge point and the predicted resources evaluation, combine two predicted results and recommend resources to user.Finally, this article design and implementation of the personalized recommendation system of education resources, and validate the validity of the algorithm. Experiments show that the algorithm can track user’s interest, recommend education resources which user really interested in.