一种基于内容和协同过滤同构化整合的推荐系统模型  

Recommender System Model Based on Isomorphic Integrated to Content-based and Collaborative Filtering

作　　者：李忠俊[1] 周启海[1] 帅青红[1] 

机构地区：[1]西南财经大学经济信息工程学院,成都610074

出　　处：《计算机科学》2009年第12期142-145,共4页Computer Science

基　　金：西南财经大学科研基金资助项目(O8YJ18)资助

摘　　要：基于内容的推荐系统和协同过滤系统是最为流行的两种推荐系统,它们都有各自的优点和缺点。提出了一种基于对这两种推荐系统同构化整合的推荐模型,该算法同时拥有协同过滤推荐系统和基于内容推荐系统的优点,并且在一定程度上避免了基于内容或协同过滤的传统推荐系统各自的缺点。实验表明,该同构化整合模型与算法比传统的简单基本推荐模型、基于内容的推荐模型和协同过滤推荐模型提高了推荐的精确率。The two recommender systems which are respectively based on content and collaborative filtering methods are most popular. Both types of filtering methods have advantages and disadvantages. This paper proposed a new isomorphic integrated model and algorithm which have the merits of the traditional recommender systems based on above two methods, and avoid the shortages of them to some extent. The experimental results show that the presented isomor- phic integrated model and algorithm can improve the performance of the traditional recommender systems in predictive accuracy.

关 键 词：同构化整合 基于内容 协同过滤 推荐系统模型 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论] TP311[自动化与计算机技术—计算机科学与技术]