一种基于内容和协同过滤同构化整合的推荐系统模型

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.

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

The two recommender systems which arc 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 isomorphic integrated model and algorithm can improve the performance of the traditional recommender systems in predictive accuracy.