Personalized question recommendation method and system using machine learning model

A technology of machine learning model and recommendation method, which is applied in the field of Internet education, can solve problems such as impossible to do and impossible to screen questions, and achieve the effect of improving grades
CN112232610AActive Publication Date: 2021-01-15北京几原科技有限责任公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
北京几原科技有限责任公司
Publication Date
2021-01-15

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Abstract

The invention discloses a personalized question recommendation method and system using a machine learning model, and the method comprises the steps: collecting the related questions of a student, building a question bank, carrying out the multi-target estimation of each question in the question bank through the machine learning model, scoring the questions in the question bank according to the estimated targets and the contribution value of each question to the total score of the student, selecting a first number of questions according to the scores, establishing a first question set, sortingthe questions in the first question set under each estimated target, converting the corresponding rank into scores, and performing fusion sorting; and selecting a second number of questions in the fusion sorting result according to set question selection factors and recommending the questions to the students. According to the invention, personalized questions can be provided for students, maximized question solving efficiency is achieved, and powerful support is provided for score improvement.
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Description

technical field

[0001] The invention relates to the technical field of Internet education, in particular to a personalized topic recommendation method and system using a machine learning model. Background technique

[0002] In order to improve test scores, the "sea of ​​questions tactic" is often adopted; students can improve their mastery of knowledge points and proficiency in questions by doing more questions. However, there are quite a few topics that students do not need to do. For example, students are already very proficient in trigonometric functions, so they can save the number of questions for such questions and save time to do other less familiar questions.

[0003] In the existing technology, the optimization of the questions for students is often based on the current general knowledge level of the students, or recommending questions to students based on knowledge points. There are definitions, and only several types of students can be artificially distinguished...

Claims

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