Skill training method and platform based on multi-dimensional data mining and dynamic recommendation

CN122243692APending Publication Date: 2026-06-19CHINA NUCLEAR POWER OPERATION TECH CORP

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: CHINA NUCLEAR POWER OPERATION TECH CORP
Filing Date: 2026-02-02
Publication Date: 2026-06-19

AI Technical Summary

Technical Problem

Existing online skills training platforms lack accurate course recommendations and fail to dynamically adapt to changes in user needs. This results in a low match between recommended content and actual user requirements, reducing learning efficiency and user experience.

Method used

Employing multidimensional data mining and dynamic recommendation methods, this approach collects user basic information, learning behavior, and social relationship data to construct user feature vectors. It then uses association rule algorithms to analyze data relationships and builds a dynamic adaptive recommendation algorithm. This algorithm adjusts recommendation strategies based on user characteristics and real-time data, combining collaborative filtering, content-based recommendation, and knowledge base boosting to monitor recommendation performance and adjust weights in real time.

Benefits of technology

This enables precise matching of user needs with training courses, improving the accuracy and personalization of course recommendations, and enhancing the user learning experience and the competitiveness of the training platform.

✦ Generated by Eureka AI based on patent content.

Smart Images

Figure CN122243692A_ABST

Patent Text Reader

Abstract

This invention relates to the field of education and training, and more particularly to a skills training method and platform based on multidimensional data mining and dynamic recommendation. The method involves: collecting multidimensional user data; preprocessing the collected raw data, then extracting key features to construct a user feature vector; using an association rule algorithm to analyze the relationships between data in each dimension; constructing a dynamic adaptive recommendation algorithm to dynamically adjust the recommendation strategy based on user characteristics and real-time data; and ranking the recommended courses according to the matching degree between the courses and user needs calculated by the recommendation algorithm, recommending the course with the highest matching degree to the user. This invention improves the accuracy and personalization of course recommendations, enhances the user learning experience and course effectiveness, and strengthens the competitiveness of the training platform.

Need to check novelty before this filing date? Find Prior Art