南京师大学报（自然科学版）Issue(1)：98-103,6.
一种改进的结合标签和评分的协同过滤推荐算法
An Improved Unifying Tags and Ratings Collaborative Filtering for Recommendation System
摘要
Abstract
The recommendation system has become the hot topic widely studied in the field of big data due to the massive a-mounts of data it contains. While the collaborative filtering algorithm is one of the most popular approach in the recommenda-tion system. When making recommendations using the traditional collaborative filtering ( CF ) algorithms based on ratings matrix,we face the problem of sparsity that seriously impairs the quality of recommendation. Meanwhile, there is a large number of tags information that describe the attribute characteristics of users and items. Integrating these tags information into the traditional recommendation algorithms is a promising means to alleviate the sparsity problem. Therefore,to address the sparsity problem,this paper proposes a new collaborative filtering recommendation algorithm that integrates the tags and rat-ings,named UTR-CF. This algorithm utilizes the tags information and the ratings data simultaneously to compute the similarity between users or items,and then generate the recommendations. The experimental results indicate that the newly developed al-gorithm can alleviate the sparsity problem,and improve the accuracy of recommendation system simultaneously.关键词
协同过滤/标签/推荐系统/稀疏性Key words
Collaborative Filtering/tag/recommendation system/sparsity分类
信息技术与安全科学引用本文复制引用
高娜,杨明..一种改进的结合标签和评分的协同过滤推荐算法[J].南京师大学报（自然科学版）,2015,(1):98-103,6.基金项目
国家自然科学基金重点、面上(61432008、61272222) （61432008、61272222）
