TFIDF算法在文章推荐系统的应用  

Application of TFIDF Algorithm in Article Recommendation System

作　　者：刘展 LIU Zhan(Guangzhou Jeagine Education Technology Co.,Ltd, Guangzhou 510000,China)

机构地区：[1]广州寄锦教育科技有限公司,广东广州510000

出　　处：《电脑知识与技术》2019年第3期17-20,共4页Computer Knowledge and Technology

摘　　要：构建文章推荐系统需要把文章向量化,然后组建一个推荐矩阵,矩阵里的元数据(数值)会影响推荐效果,如何使文章推荐矩阵元数据更好地与用户行为关联起来,这里提出了基于TFIDF算法关联到用户行为的表示和更新机制,根据用户行为的特点,给予不同的权重,最终会影响到元数据的取值,进而能量化用户行为标签的兴趣值,另一方面也能产生更好的推荐效果。Static Building the article recommendation system needs to vectorize the article, and then form a recommendation matrix.The metadata(value) in the matrix will affect the recommendation effect. How to make the article recommendation matrix metadata better correlated with user behavior, here is based on TFIDF, The algorithm is associated with the representation and update mechanism of the user’s behavior. According to the characteristics of the user’s behavior, different weights are given, which ultimately affects the value of the metadata, thereby enlightening the interest value of the user’s behavior tag, and on the other hand, it can produce better recommended effect.

关 键 词：TFIDF算法 余弦相似度 文章推荐系统 推荐矩阵 数据建模 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]