基于标签与协同过滤算法的淘书吧应用

随着互联网的不断发展,电子商务的流行使人们从线下交易逐渐转为线上交易。电子商务中的推荐系统对人们日益多元化的网络消费起到了至关重要的作用。本文在传统协同过滤推荐算法基础上,加入商品标签属性,构建用户,商品,标签三者之间的关联模型。先构建用户商品评分矩阵,在计算用户对商品兴趣度时增加入标签作为权重系数,提高淘书吧应用推荐准确性。实验结果表明,该方法能有效地改进现有的推荐算法,达到更好的推荐效果。

TaoShuBa application based on tag and collaborative filtering algorithm

With the continuous development of the Internet, the popular e-commerce transactions so that people from the line turning into online transactions. E-commerce recommendation system for growing a wide range of network consumption has played a crucial role. In this paper, the traditional collaborative filtering recommendation algorithm based on adding merchandise tag attributes, building association model users, merchandise, labels between the three. First build a user merchandise scoring matrix, in calculating the user to increase the degree of interest in the product label into a weighting factor to improve the accuracy of Taoshu applications recommended it. The experimental results show that this method can effectively improve the existing recommendation algorithm to achieve better recommendation results.