| As one of the classical algorithms in recommendation algorithms,collaborative filtering algorithm is a common solution for E-learning platform to solve course recommendation,but it also has the problems of cold start,long calculation time and low accuracy of course recommendation.In order to solve the problems in the application of collaborative filtering recommendation algorithm in E-learning,this dissertation proposed a course recommendation algorithm based on fuzzy clustering and collaborative filtering(SAFCMCF),which improves the user-based collaborative filtering recommendation algorithm and provides users with more accurate course recommendation service.Firstly,in order to solve the problem that the fuzzy clustering algorithm is easy to fall into the local optimum,the principle and process of the fuzzy clustering algorithm are deeply studied,and the simulated annealing algorithm is analyzed in detail,and it is applied to the process of improving the fuzzy clustering algorithm.Improved fuzzy clustering algorithm to cluster users.The effectiveness of the improved fuzzy clustering algorithm is verified on the real data set,and the experiments prove that the improved fuzzy clustering algorithm proposed in this dissertation is superior to the traditional fuzzy clustering algorithm.Secondly,in order to optimize the course recommendation results,a course recommendation algorithm based on fuzzy clustering and collaborative filtering is proposed by analyzing the characteristics of users of the E-Learning platform.The algorithm first cleans and processes the data set,extracts relevant feature indicators,analyzes the user’s implicit behavior data,establishes a user-course rating mapping,and comprehensively evaluates the user’s behavior preference;Then the similarity degree of users in the same cluster and the nearest neighbor set of target users are calculated to predict the scores of target users who have not watched the course.Finally,the course recommendation algorithm based on fuzzy clustering and collaborative filtering is applied to E-learning platform to realize personalized recommendation of course resources according to users’ scoring preferences,so as to help users better complete course Learning.Experiments show that the SAFCMCF algorithm proposed in this dissertation has smaller error and higher accuracy of recommendation compared with traditional collaborative filtering algorithm,traditional fuzzy clustering algorithm and collaborative filtering algorithm.Finally,the course recommendation algorithm based on fuzzy clustering and collaborative filtering studied in this paper is used as the core recommendation algorithm of the system,and it combines the popular course recommendation methods based on statistics,and is applied to E-Learning course recommendation system based on SpringBoot and VUE framework design,which can recommend courses that meet users’interests and preferences. |
