Abstract:

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.

Key words: information retrieval, social annotation, personalized ranking, preference of topic domains