Department of Nursing, Zhengzhou Health Vocational College, Zhengzhou 450122, China.
Ophthalmology Department, People´s Hospital of Zhengzhou, Zhengzhou 450122, China.
Comput Intell Neurosci. 2022 Oct 11;2022:7057437. doi: 10.1155/2022/7057437. eCollection 2022.
The traditional nursing teaching knowledge point recommendation algorithm based on collaborative filtering is difficult to deal with the problem of data sparsity, while the traditional recommendation algorithm based on matrix decomposition has poor scalability in dealing with high-dimensional data, and their recommendation results are only determined according to the prediction score, resulting in low recommendation accuracy. In view of this, a nursing teaching knowledge point recommendation method based on a SOM neural network and ranking factor decomposition machine is proposed. Firstly, the SOM neural network is used to cluster users based on users' academic background information, then the partial order relationship of nursing teaching knowledge points is constructed by using users' explicit and implicit web access behavior, and finally, the factor decomposition machine is used as the ranking function to classify users' academic background web access behavior, borrowing nursing teaching introduction text, and other characteristic information were modeled, and the peer-to-peer ranking learning algorithm was used to accurately recommend nursing teaching knowledge points. Experimental results show that the proposed method can effectively alleviate the problem of data sparsity and improve the accuracy and efficiency of recommendations.
基于协同过滤的传统护理教学知识点推荐算法难以处理数据稀疏性问题，而基于矩阵分解的传统推荐算法在处理高维数据时可扩展性差，其推荐结果仅根据预测得分确定，导致推荐精度低。针对这一问题，提出了一种基于 SOM 神经网络和排序因子分解机的护理教学知识点推荐方法。首先，利用 SOM 神经网络根据用户的学术背景信息对用户进行聚类，然后利用用户的显式和隐式网络访问行为构建护理教学知识点的偏序关系，最后，将因子分解机作为排序函数对用户的学术背景网络访问行为进行分类，借鉴护理教学介绍文本等特征信息进行建模，并采用点对点排序学习算法进行准确的护理教学知识点推荐。实验结果表明，所提方法能有效缓解数据稀疏性问题，提高推荐的准确性和效率。
