Method and system for recommending content according to evaluation result

Abstract

The invention discloses a method and system for recommending content according to an evaluation result, and the method comprises the following steps: responding to an evaluation starting signal, generating an evaluation question matched with a user for the user, and starting a camera; in response to the evaluation ending signal, uploading an evaluation result of the user and a video recorded by the camera to a remote database; extracting an evaluation result; extracting user face information from the recorded video; performing intelligent analysis on the evaluation result and the user face information, and outputting a final evaluation result; and recommending content to the user according to the final evaluation result. When content recommendation is carried out according to the test result, the facial expression analysis of the user is fused, so that the evaluation effectiveness can be enhanced, and the self-promoted content can be intelligently recommended.

Description

technical field [0001] The present application relates to the field of computers, in particular to a method and system for recommending content according to evaluation results. Background technique [0002] As one of the most popular learning methods at present, online course learning provides learning users with a wide range of resources and an open platform to a certain extent, but the current learning method also has some obvious drawbacks, that is, the way of using evaluation forms Recommend matching courses for users. In the process, judge and analyze the psychological state of the testees according to the scores of the answers. The current psychological assessment scales generally have standard, fault-tolerant, standardized formats, and scoring Characteristics such as method and result interval judgment criteria generally remain at the stage of question and answer, manual statistics, and imprecise analysis. [0003] The existing psychological assessment scale itself d...

AI Technical Summary

Problems solved by technology

[0002] As one of the most popular learning methods at present, online course learning provides learning users with a wide range of resources and an open platform to a certain extent, but the current learning method also has some obvious drawbacks, that is, the way of using evaluation forms Recommend matching courses for users. In the process, judge and analyze the psychological state of the testees according to the scores of the answers. The current psychological assessment scales generally have standard, fault-tolerant, standardized formats, and scoring The characteristics of methods and result interval judgment standards are generally still in the stage of question and answer, manual statistics, and imprecise analysis

[0003] The existing psychological assessment scale itself does not have the ability to reflect individual information, and the traditional psychological assessment method will become more and more difficult in input, statistics, and analysis as the workload increases, so the error rate will increase

At the same time, if the assessors are resistant to the psychological assessment, they will provide wrong answers when answering the questions.

These problems will directly lead to a reduction in the accuracy of the final result, thereby reducing the credibility and accuracy of the psychological evaluation.

Therefore, the existing system for recommending courses based on the evaluation form is prone to deviations between the above scale options and the real situation, and cannot correct the evaluation results. The traditional method cannot verify the validity of the user's answering psychology, and cannot intelligently recommend. Promote user content

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