融合热门度因子基于标签的个性化图书推荐算法

Personalized Tag-based Book Recommendation Algorithm Combined with the Factor of Popularity

依照Web2.0的"社会化标注"思想,针对基于内容的推荐算法(CBR)和协同过滤推荐算法(CF)存在的不足,提出了基于读者标签(Tags)的、融合图书"热门度"因子的个性化图书推荐的两个改进算法。利用统计分析软件R,重点对改进后的CBR算法进行实验分析和验证,结果表明,改进算法的图书个性化推荐效果有明显改善。

In accordance with ＂socialized tagging＂ of Web 2.0 and in view of the shortcomings of the content-based recommendation algorithm （CBR） and the collaborative filtering recommendation algorithm （CF）, we proposed two refined book recommendation algorithms which are based on tags and combined with the factor of popularity. In this paper, a small-scale experiment using statistical analyzing software R was made along with an analysis of the refined content-based recommendation algorithm （NCBF）. The experimental results show that the refined algorithms remarkably improved the effectiveness of personalized recommendation algorithm.