一种融合社会化标注系统中主题域相似的个性化排序方法

摘要

随着网络技术的发展,互联网中越来越多的资源被应用于信息检索中,大量的研究表明,社会化标注可以用于改善信息检索.现有个性化排序的方法中,用户之间的相似度大多通过其共同使用过的标签集来计算.然而,现实中用户标注数据存在稀疏性和标签同义词等问题,导致相似度计算并不准确.在前人研究的基础上,提出了一种融合主题域相似的个性化排序方法.该方法首先通过主题域的划分,将不同主题含义的网页及标签分开,通过构建的标签相似网络找出标签同义词.然后结合用户标签和主题偏好找出兴趣相近的用户,并对用户的标注信息进行扩展,从而能够有效地改善个性化信息检索的效果.在真实数据上的实验结果表明,该方法能有效缓解标注稀疏性和标签同义词问题,有助于改善用户检索体验.%With the development of network technology,more and more resources are applied in information retrieval in the Internet.Numerous studies show that the social annotation can be used to improve search quality.In the existing personalized ranking methods,the similarity between users is usually calculated by their commonly used tag sets.However,in reality,there are some problems such as the sparseness of user annotation data and label synonyms,which makes the similarity calculation inaccurate.Based on the previous researches,this paper proposes a personalized ranking method for fusing the similar topic domains.Firstly,this method separates the webpage and tags with different thematic meanings,and finds the tag synonyms by constructing the network of similar tags.Secondly,this method finds the users of similar interests by combing the user's tags and the preference of topic domains,and extends the user's tag information to improve the personalized information retrieval effectively.Experimental results on real data show that this method can effectively alleviate the problems of data sparsity and tag synonyms,and can help to improve the user's search experience.