一种基于HITS和随机跳转的网页标签排序方法

A Tag Ranking Method Based on HITS and Random Walk

Web 2.0应用的兴起,推进了情报学科由＂文献组织＂向＂知识组织＂演化。网页标签作为重要的Web2.0应用之一,已经成为大众组织知识的常用途径。然而,现有的标签排序方法难以有效满足知识组织的需求。本文在三核协同标签模型的基础上,充分考虑标签和用户、标签和标签、标签和文档之间的关系,提出了一种结合HITS和随机跳转的标签排序方法。该方法利用高质量标签和高质量用户之间的相互加强关系,根据标签之间的相似性来找出高质量相关标签,有效提高标签排序的质量。在Delicious数据集上的实验结果表明,该方法能较大提高标签排序的准确度。

With the rise of Web 2.0 applications,a new trend in information science,namely the evolution from＂organizing document＂to＂organizing knowledge＂,is looming on the horizon.One of important Web 2.0 applications, social tag,is making this trend a reality by adding meaningful annotations to Web pages.However,existing tag ranking methods are not efficient in knowledge organization.To improve tag ranking performance,this paper proposes a new ranking algorithm by utilizing relationships among users,tags and Web documents in a tripartite collaborative tagging model.By combining HITS and random walk,we effectively exploit the mutual reinforcement between quality users and quality tags and retrieve related tags by measuring similarity between tags.Experimental results on Delicious dataset demonstrate the effectiveness of our algorithm.