Abstract
This paper presents a personalized course recommended algorithm based on the hybrid recommendation. The recommendation algorithm uses the improved NewApriori algorithm to implements the association rule recommendation, and the user-based collaborative filtering algorithm is the main part of the algorithm. The hybrid algorithm adds the weight to the recommendation result of the user-based collaborative filtering and association rule recommendation, implementing a hybrid recommendation algorithm based on both of them. It has solved the problem of data sparsity and cold-start partially and provides a academic reference for the design of high performance elective system. The experiment uses the student scores data of a college as the test set and analyzes results and recommended quality of personalized elective course. According to the results of the experimental results, the quality of the improved hybrid recommendation algorithm is better.
Highlights
In recent years, the research of education data mining in school teaching has arisen gradually, such as clustering analysis, genetic algorithm, association rule mining, collaborative filtering recommendation algorithm, etc
This paper proposes a hybrid recommendation algorithm based on the improved NewApriori algorithm for association rule mining and collaborative filtering course selection algorithm, which is recommended for students to select classes
This paper presents a hybrid recommendation algorithm based on the improved NewApriori association rules and collaborative filtering
Summary
The research of education data mining in school teaching has arisen gradually, such as clustering analysis, genetic algorithm, association rule mining, collaborative filtering recommendation algorithm, etc. The traditional project-based collaborative filtering recommendation algorithm still can't find the users' similarity of different evaluation items, and it is not very effective in seeking multiple content items. Some scholars have proposed a collaborative filtering algorithm based on project attributes[5] This algorithm calculates the similarity of project attributes to recommend for users, which has solved the problem of cold-start of the project and the sparsity of rating data, but the algorithm still has the problem of cold-start for new users. The hybrid recommendation algorithm studied in this paper can solve the problem of missing data to a certain extent It can predict the students' achievement data for a missing course and solve the cold-start problem of new projects
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