Abstract: In the current complex teaching interactive environment of AI, aiming at the dimension disaster and high data updating cost faced by the learner portrait model, this paper proposes a new model—low-cost self evolutionary learner portrait under large-scale data（LSLP）. This method first improves the traditional deep nonnegative matrix decomposition algorithm, so as to preserve the feature structure of the original data from the double space, effectively reduce the dimension and suppress the dimension disaster. Then, taking the graph neural network as the information capture medium, combined with the depth neural network to quantify the meta attribute state value, an adaptive feature extraction and dynamic update strategy is designed to assist the learner portrait model to evolve continuously. Finally, four experiments are designed on the data set of Stanford EDX platform to verify the performance of this model. The experimental results show that this model can reduce the cost of updating data by 45% with 93.13% accuracy of downstream teaching recommendation tasks.

Key words: learner portrail, double space dimensionality reduction, graph attention network, adptive feature extraction, low data cost update

摘要： 复杂教学交互环境下，针对学习者画像模型所面临的维数灾难和更新高数据成本问题，提出了一种新的模型——大规模数据下低成本自进化学习者画像，该方法改进了传统的深度非负矩阵分解算法，以此来对原始数据从双空间进行特征结构保留并有效降维，抑制维数灾难；以图神经网络为信息捕获媒介，结合深度神经网络对元属性状态值进行量化，引导设计了一种自适应的特征抽取与动态更新策略来辅助学习者画像模型不断自进化；在斯坦福EDX平台数据集上设计了四项实验以验证该模型的性能。实验结果表明，该模型在93.13%的下游教学推荐任务精度下，可减少45%的更新数据成本。

关键词: 学习者画像, 双空间降维, 图注意力网络, 自适应特征抽取, 低数据成本更新