Research on rehabilitation training program recommendation system based on ALBERT-LDA model
ZHU Xiaozhuang
XU Qianqian
GAO Nuo
Abstract：In order to solve the problem of inaccurate semantic expression and low recommendation accuracy rate of training scheme caused by ignore contextual semantic information and potential semantic in body evaluation in traditional rehabilitation trainig schemes,we proposed a deep learning network model(ALBERT-LDA)integrating ALBERT and latent Dirichlet allocation(LDA).Based on this model,a recommendation system for physical exercise rehabilitation training was constructed.Firstly,the system used the LDA topic model and ALBERT model to obtain the document-level topic information and the word-level semantic representation,respectively.Secondly,TextCNN was used to extract the semantic features of body evaluation text at word level,and the extracted features were re-constructed through hierarchical attention mechanism.Finally,a multiple fusion strategy was used to fuse the reconstructed features with the subject features to recommend training schemes.The experiment showes that the proposed system can not only obtain deeper seman-tic features,but also understand the contextual semantics more comprehensively,which can provide more objective and effective reha-bilitation training programs for patients.
Keywords：Recommendation of rehabilitation training programALBERT modelLDA topic modelFeature fusion
Publication Date：2024-12-28
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：11( 445-455 )