Course recommendation method and device based on time varying and collaborative filtering, equipment and medium

Abstract

The invention relates to the field of artificial intelligence, and provides a course recommendation method and device based on time varying and collaborative filtering, equipment and a medium, which can construct an initial feature matrix according to to-be-processed data, associate features of a user and a course, realize interaction between the user features and course features and construct a correction factor based on a latent semantic model. global features, user features and course features are comprehensively considered to correct an initial feature matrix, collaborative filtering is carried out on the basis of correction based on time-varying factors, so that courses are scored more reasonably, the courses are scored based on the time-varying factors and collaborative filtering, and accurate recommendation of the courses is realized. In addition, the invention also relates to a block chain technology, and the target matrix can be stored in a block chain node.

Description

Technical field[0001]The present invention relates to the field of artificial intelligence, and more particularly to a course recommendation method, apparatus, apparatus, and media based on time-beam and synergistic filtration.Background technique[0002]At present, in order to improve the overall level of the salesperson, it is necessary to regularly carry out training for salespersons. How to "have a large number of training programs, the training content is too standardized and the training tutor" is the most appropriate and Training that meets the current appeal has become a primary problem for training.[0003]In the prior art plan, in order to solve the above problems, it is mainly employed by artificial development courses, or the recommendation of training courses in collaboration models. However, the salesman's demand for training is dynamically changed, and the course itself is also dynamically changed over time.[0004]In the face of these two changes, artificial development co...

AI Technical Summary

Problems solved by technology

However, the salesperson's demand for training changes dynamically over time, and the course itself also changes dynamically over time

[0004] In the face of these two changes, artificial curricula are highly subjective and not immediate enough to adapt to the above two changes; the current collaborative filtering model cannot capture the dynamic changes of these two characteristics over time

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