基于大数据分析的中医药信息个性化推荐系统

Personalized Recommendation System of Traditional Chinese Medicine Information Based on Big Data Analysis

为解决中药智能推荐存在的稀疏性和扩展性问题,设计基于大数据分析的中医药信息个性化推荐系统。首先采集患者中药历史用药数据,并保存到存储器当中,利用处理器运行改进后的协同过滤算法,检索相关中医药信息,最后将结果反馈给目标用户,实现针对目标用户的个性化推荐。结果表明:设计系统的推荐覆盖指数较高,说明稀疏性问题得到了改善,提高了个性化推荐质量;随着最近邻居个数增加,系统中改进后个性化推荐算法的执行时间复杂度减小,说明扩展性问题得以改善。

In order to solve the problem of sparsity and expansibility in intelligent recommendation of traditional Chinese medicine,this paper designs a personalized recommendation system of TCM information based on big data analysis.First,the data of the patients'historical Chinese medicine was collected and saved in the memory.The improved collaborative filtering algorithm was used to retrieve the relevant TCM information.Finally,the results were fed back to the target users to make personalized recommendation for the target users.The results show that the recommended coverage index of the design system is high,which indicates that the sparsity problem is improved and the personalized recommendation quality is improved;With the increase in the number of neighbors recently,the complexity of the implementation time of the improved personalized recommendation algorithm is small,which indicates that the scalability problem is improved.