个性化特征的电子商务智能推荐系统

以准确向用户推荐商品,提升电子商务网站销售量为目标,设计基于个性化特征的电子商务智能推荐系统。系统以个性化推荐引擎为核心,采集交易事务、商品特征、用户评价等数据,利用基于个性化特征的协同过滤推荐算法计算商品间相似度,确定新商品的近邻,根据近邻用户对新商品的评价结果选择商品进行推荐。测试结果表明,该系统的电子商务商品推荐误差小,有利于提升电子商务网站交易率,而且电子商务商品推荐性能明显优于其他推荐系统。

Personalized e-commerce intelligent recommendation system

In order to accurately recommend goods to users and improve the sales volume of e-commerce websites,an intelligent e-commerce recommendation system based on personalized features is designed.The system takes the personalized recommendation engine as the core,collects the transactions,commodity characteristics,user evaluations and other datas,uses collaborative filtering recommendation algorithm based on personalized features to calculate the similarity between products,determines the neighbors of new products,predicts the user’s evaluation of new products according to the evaluation results,and selects products for recommendation according to the evaluation results.The test results show that the e-commerce product recommendation error of the system is small,which is beneficial to improve the transaction rate of e-commerce website,and the e-commerce commodity recommendation performance is obviously better than other recommendation systems.