A Personalized Recommendation Method Based on Online Course User Data

Abstract

The invention discloses a personalized recommendation method based on user data of on-line courses. The method comprises following steps of: 1), establishing label mapping relations among courses, labels and titles; 2), calculating an error rate list ET of a user to courses by a course recommendation system according to learning records of the user; 3), generating error lists of all courses according to the list ET and calculating the similarity between two courses according to error lists of courses; and 4), as for each user, calculating prediction weight of error rate of each course of the user according to similarity; calculating recommendation weight of each label according to mapping relations and prediction weight of error rate; calculating recommendation weight of each title according to mapping relations and label recommendation weight and then generating a recommendation list for the user according to recommendation weight of titles. The personalized recommendation method based on user data of on-line courses not only helps to solve a cold start problem but also actively attracts attention of the user in order to improve learning motivation.

Description

technical field [0001] The present invention relates to the field of personalized prediction and recommendation. Based on the historical learning data of online course website users, the prediction of error peak courses is performed, and the personalized recommendation of expanded learning content under corresponding label mapping, such as challenge questions, etc. is performed. Specifically, it is a personalized content recommendation method based on online course user data. Background technique [0002] As one of the most popular learning methods at present, the online course learning website provides a wide range of resources and an open platform for learning users to a certain extent, but the current learning method also has some obvious disadvantages: 1. Accompanying learning The method can easily make users judge their own learning status vaguely; 2. The expansion of resources and information will make users blind to the content they want to learn; 3. Provide undiffere...

AI Technical Summary

Problems solved by technology

Apply the traditional collaborative filtering recommendation method to the personalized feedback of user learning on the online course learning platform, and solve the cold start problem of users with too little information

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