基于云计算的大数据处理方法及云计算平台
By constructing a course knowledge graph and student feature vectors, combined with a multi-objective deep model, the problem of insufficient personalized recommendations in online education is solved, achieving efficient and accurate course recommendations and optimizing resource allocation and learning experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 南京弘竹泰信息技术有限公司
- Filing Date
- 2025-06-06
- Publication Date
- 2026-07-17
AI Technical Summary
In existing online education systems, recommendation algorithms lack personalization, resulting in low satisfaction with recommendation results, underutilization of high-quality resources, over-recommendation of low-quality resources, and uneven resource allocation, which affects users' learning efficiency.
We construct a course knowledge graph and student feature vectors, predict students' course completion probability and test scores through a multi-objective deep model, generate a personalized recommendation candidate set, and optimize resource allocation and recommendation accuracy by combining course feature vectors and student similarity calculations.
It improves the accuracy and efficiency of course recommendations, enhances students' learning motivation and satisfaction, optimizes resource allocation, alleviates the cold start problem, and provides data-driven decision support for educational institutions.
Smart Images
Figure CN120672525B_ABST