Dynamic Content Recommendation Model for Adaptive Learning Efficiency
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Solution Overview
Problem
Existing recommendation methods in personalized learning products lack flexibility and adaptability to individual user differences, failing to maximize learning efficiency and knowledge improvement in the shortest time.
Innovation Solution
A content output method that uses a pre-trained content recommendation model to predict the expected benefit of candidate content items based on historical data and user capability attributes, recommending the most suitable content items to enhance learning efficiency and knowledge improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed accuracy rate questions are recommended for each user, then the recommendation system is simple to implement, but it lacks flexibility and cannot adapt to different individual adaptability
Solution Approach 1:
The patent implements dynamic content recommendation by transitioning from fixed accuracy rate questions to a dynamic model that adjusts question difficulty based on real-time user performance. The system continuously updates the knowledge mastery degree and selects subsequent questions adaptively, making the recommendation process flexible and responsive to individual user progress rather than static and uniform.
Solution Approach 2:
The patent changes the parameter of question selection from fixed accuracy rate to dynamic knowledge mastery degree. By calculating the degree of user knowledge mastery on each knowledge point and using this as the basis for selecting subsequent questions, the system adapts to individual user differences. This parameter change enables the system to adjust recommendation strategies based on actual user performance rather than predetermined fixed values.
2Productivity
If traditional recommendation methods are used, then the system is easy to operate, but it cannot maximize learning efficiency and knowledge improvement in the shortest time
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors user answer correctness, updates the knowledge mastery degree for each knowledge point, and uses this feedback to select subsequent questions. This closed-loop feedback process ensures that the recommendation is continuously optimized based on actual user performance, maximizing learning efficiency by focusing on areas where the user needs improvement while tracking progress in real-time.
Solution Approach 2:
The patent performs preliminary calculation of the degree of knowledge mastery for each knowledge point before selecting the next question. By pre-calculating these metrics based on user history and using them to guide question selection, the system proactively optimizes the learning path rather than reactively adjusting after failures. This preliminary action enables efficient learning by anticipating user needs and preparing appropriate content in advance.
Data Source
AI summary
Provided are a content output method and apparatus, a computer-readable medium, and an electronic device. The method includes: obtaining information of each of candidate content items for a user; predicting, based on the information of each of the candidate content items by invoking a pre-trained content recommendation model, an expected benefit for each of the candidate content items resulting from a user's operation on each of the candidate content items, in which the pre-trained content recommendation model is trained historical data of the user, and the historical data includes an actual benefit resulting from a user's operation on a historical content item and information of the historical content item; and outputting a first content item based on the expected benefit for each of the candidate content items.