摘要
利用聚类方法计算用户相似度,结合协同过滤思想构建个性化推荐算法,对中药材电子商务交易数据的样本进行实验验证,实验证明融合聚类和协调过滤的推荐算法在推荐效果方面优于传统的协同过滤推荐算法.将此算法运用于中药材电子商务交易平台中,构建个性化推荐模块,提高平台的个性化服务能力.
The user similarity is calculated by the clustering algorithm, with which the collaborative filtering is combined to build the personalized recommendation algorithm to verify the sample data of e-commerce trading platform of Chinese herbal medicine. The test results show that fusion clustering and collaborative filtering recommendation algorithm is superior to the traditional algorithm in effect of the recommendation. By building personalized recommendation module, this algorithm can be applied to e-commerce trading platform in Chinese herbal medicine to improve service ability.
出处
《广西科技大学学报》
2015年第4期49-53,共5页
Journal of Guangxi University of Science and Technology
基金
安徽省振兴计划高等学校优秀青年人才重点基金资助项目(2013SQRL129ZD)资助
关键词
聚类
协同过滤
个性化服务
中药材交易
clustering
collaborative filtering
personalized service
Chinese herbal medicine trade
