Reading recommendation large model generation method and system based on big data resources

By constructing a large-scale reading recommendation model based on big data resources, the problem of mismatch between students' reading material selection and actual learning in educational practice has been solved, achieving accurate reading recommendations and improving learning outcomes and the system's adaptability.

CN121681933AActive Publication Date: 2026-03-17SOUTH CHINA NORMAL UNIV
5 Cites 1 Cited by

Patent Information

Application Number
CN202511799849.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-02
Publication Date
2026-03-17
Estimated Expiration
2045-12-02

AI Technical Summary

Technical Problem

Existing technologies make it difficult to accurately select appropriate reading materials for students in educational practice, resulting in a mismatch between learning materials and students' cognitive levels, increasing the learning burden or causing students to miss growth opportunities.

Method used

By employing a method for generating a large-scale reading recommendation model based on big data resources, including constructing a validated recommendation database, low-rank adaptive fine-tuning of the domain-specific model, multidimensional analysis and semantic vector retrieval, weighted fusion and deduplication ranking, an adaptively optimized reading recommendation system is formed.

Benefits of technology

It achieves precise alignment with students' cognitive levels, improves learning outcomes, ensures the accuracy, robustness, and interpretability of recommendation results, and possesses the ability to continuously learn and evolve.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of large models, in particular to a reading recommendation large model generation method and system based on big data resources. The method comprises the following steps: acquiring a to-be-assessed text, collecting reading materials with multiple learning segments and multiple styles, labeling the reading materials, carrying out aggregation and consistency verification on labeling results, and constructing a verified recommendation database; performing low-rank adaptive fine tuning on the general pre-training large model based on the recommendation database to obtain a domain model, and performing performance and deviation monitoring and correction in the training process; performing multi-dimensional analysis on a to-be-evaluated text to generate a text feature vector, and performing mixed retrieval on a recommendation database in combination with semantic vector similarity retrieval; according to the invention, through the construction of the large reading recommendation model, the reading recommendation is more accurate and efficient.
Need to check novelty before this filing date? Find Prior Art