信息智能推荐系统目标特征数据挖掘方法

Target feature data mining method for information intelligent recommendation system

为提高信息智能推荐系统的目标特征数据挖掘精度,提出新的信息智能推荐系统目标特征数据挖掘方法。采用分布式链路融合方法构建数据存储模型,在多维特征空间中实现对特征数据的解析,基于此分析目标特征分布集,根据大数据的关联规则分布特性,实现数据智能融合处理。采用显著度特征解析控制的方法提取目标特征模板匹配特征量,实现信息智能推荐系统目标特征数据的挖掘。实验结果表明,所提目标数据挖掘方法准确性较高,且信息智能推荐系统目标特征数据挖掘过程具有较为理想的聚类性。

In order to improve the accuracy of target feature data mining in information intelligent recommendation system,a new method of target feature data mining in information intelligent recommendation system is proposed.The distributed link fusion method is used to construct the data storage model,and the feature data is parsed in the multi-dimensional feature space,Based on the analysis of the target feature distribution set,the intelligent data fusion processing is realized based on the distribution characteristics of the association rules of big data.The method of salient feature analytic control is used to extract the target feature template matching feature quantity to realize the mining of the target feature data in the information intelligent recommendation system.The experiment results show that the proposed target data mining method has high accuracy,and the data mining process of the target features of the information intelligent recommendation system have relatively ideal clustering.