基于遗传模糊聚类的化工产品电商个性化推荐算法研究

Research on Personalized Recommendation Algorithm of Chemical Product E-commerce Based on Genetic Fuzzy Clustering

文章基于一种遗传模糊聚类的电商个性化推荐算法,对电商的销售、个性化推荐、满足用户需求和购买匹配性等性能方面展开研究。根据电商个性化偏好推荐信息进行数据收集,提取个性化数据的关联特征量进行聚类处理,再结合模糊B均值聚类方法实现电商个性化推荐。根据电商个性偏好对采集数据样本进行差值拟合和结构重组,并采取遗传进化方法全局寻优。结果显示,利用该算法进行电商个性化推荐不仅满意度高,准确度和置信度也获得一致好评。

Based on a genetic fuzzy clustering algorithm for e-commerce personalized recommendation,this paper studies the performance of e-commerce such as sales,personalized recommendation,user demand and purchase matching.According to e-commerce personalized preference recommendation information for data collection,the associated feature quantity of the personalized data is extracted for clustering processing,and the fuzzy B-means clustering method is combined to realize the personalized recommendation of e-commerce.According to the personal preference of e-commerce,the difference fitting and the structure reorganization of the collected data samples are carried out,and the genetic evolution method is adopted to optimize the whole situation.The results show that the algorithm for personalized e-commerce recommendations not only has high satisfaction,but also has high accuracy and confidence.