A Personalized Recommendation Method Based on Online Course User Data

A user data and recommendation method technology, applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as cold start of users with too little information, solve the problem of cold start, improve learning motivation, and increase the number of courses Effect

Active Publication Date: 2018-11-20
BEIJING JUDAOYOUDA NETWORK TECH
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  • Abstract
  • Description
  • Claims
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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

Method used

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  • A Personalized Recommendation Method Based on Online Course User Data
  • A Personalized Recommendation Method Based on Online Course User Data
  • A Personalized Recommendation Method Based on Online Course User Data

Examples

Experimental program
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Embodiment Construction

[0039] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0040] Based on the integration and processing of the original learning data, the present invention predicts the N courses with the highest error rate from the user's real error rate and the similarity between courses, performs weighted voting to calculate the recommendation weight of the extracurricular challenge topic under the corresponding label mapping, and obtains the recommendation list.

[0041] Taking an online programming learning website as an example to further illustrate, the main steps are as follows:

[0042] 1. Preprocessing of the original user data to obtain the user's error list: take out the original learning records from the database, and analyze the user's submission operation data for the course (cid, uid, active_type, active_text, time)

[0043] Each parameter represents: cid: course unique id, uid: learning user unique id, acti...

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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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 周英华张茜杨斌俞昊然孙广中
Owner BEIJING JUDAOYOUDA NETWORK TECH
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