Online resource adaptive recommendation method for multi-modal learning behavior analysis

Through heterogeneous sensor arrays and deep learning technology, an adaptive online resource recommendation system is built, which solves the problem of insufficient data acquisition and processing in the existing technology, realizes personalized resource recommendation, and improves learning effect and platform efficiency.

CN120561380APending Publication Date: 2025-08-29SHANDONG LENSI EDUCATION TECH (GRP) CO LTD
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Patent Information

Application Number
CN202510734257.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-04
Publication Date
2025-08-29

AI Technical Summary

Technical Problem

The existing online resource recommendation methods cannot fully and accurately understand learners' multimodal learning behaviors and needs, data collection is not systematic and dynamic enough, data processing and analysis are not in-depth enough, and the recommendation model lacks adaptability and dynamic optimization capabilities, resulting in a gradual decline in recommendation effect.

Method used

Heterogeneous sensor arrays are used to collect multimodal data, and data preprocessing is performed through Transformer noise reduction autoencoder and attention mechanism, and a hierarchical feature extraction network and learning behavior generation model are built, combining the recommendation decision framework for reinforcement learning and adaptive adjustment strategies to achieve personalized resource recommendation.

Benefits of technology

It realizes in-depth exploration of learners' behavior patterns and status assessment, provides accurate and efficient resource recommendation services, improves learning effect and user experience, and improves the resource utilization rate of online learning platforms.

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Abstract

The invention discloses an online resource adaptive recommendation method based on multi-modal learning behavior analysis, and relates to the technical field of resource recommendation. The method comprises the following steps: firstly, dynamically collecting multi-modal data by using a heterogeneous sensor array, and carrying out noise reduction, probability distribution matching normalization and time-space alignment preprocessing; features are extracted through a hierarchical network, modeling learning behaviors such as a variational auto-encoder are combined, and the learning state is evaluated from multiple dimensions; recommendation decisions are generated based on reinforcement learning, recommendation is optimized in combination with personalized presentation and multi-source feedback analysis, meanwhile, the system has the functions of dynamic strategy adjustment, intelligent resource creation, cross-scene migration recommendation and the like, and accurate self-adaptive recommendation is achieved. According to the method, multi-modal data are comprehensively collected and deeply processed, learning behaviors and evaluation states are accurately analyzed, personalized resource recommendation is provided through intelligent recommendation and dynamic optimization strategies, recommendation accuracy and learning effects can be improved, user experience can be enhanced, and the utilization rate and competitiveness of platform resources can be improved.
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