A method, apparatus, device, and storage medium for determining a course recommendation list.

By constructing a target-based basic data dictionary and sparse vectors, and combining multi-dimensional employee feature information, a course recommendation list is determined, which solves the problem of narrow recommendation scope in existing technologies and achieves higher quality and more accurate course recommendations.

CN115563390BActive Publication Date: 2026-05-26AGRICULTURAL BANK OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
AGRICULTURAL BANK OF CHINA
Filing Date
2022-10-20
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing course recommendation algorithms based on Bayesian probability statistics rely too heavily on existing user behavior data. As the number of executions increases, the range of recommended data decreases, making it easy to recommend only courses that users are interested in, rather than effectively recommending specific courses that match job requirements.

Method used

By acquiring sample data, a target basic data dictionary is constructed, resulting in a target sparse vector and an initial feature vector of the objects to be recommended. The target basic data dictionary and feature vectors are then used to determine the course recommendation list, taking into account the feature information of employees in various dimensions.

Benefits of technology

This improves the quality and accuracy of course recommendations, avoids scenarios where only courses of personal interest are recommended, and ensures that specific courses that match the job requirements are recommended.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a method, apparatus, device, and storage medium for determining a course recommendation list. The method includes acquiring sample data; obtaining a target basic data dictionary based on the sample data; obtaining a target sparse vector based on the sample data and the target basic data dictionary; acquiring an initial feature vector of the object to be recommended; obtaining a target feature vector of the object to be recommended based on the target basic data dictionary, the target sparse vector, and the initial feature vector of the object to be recommended; and determining a course recommendation list for the object to be recommended based on the target basic data dictionary and the target feature vector. The technical solution of this invention provides a new method for determining a course recommendation list, fully considering the characteristic information of employees from various dimensions, effectively avoiding the scenario of only recommending courses of personal interest during subsequent course recommendations, and improving the quality and accuracy of course recommendations.
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