Topic exploration and distillation for web search by a similarity-based analysis

Topic distillation is the process of finding representative pages relevant to a given query. Well-known topic distillation approaches such as the HITS algorithm have shown to be useful in identifying high quality pages. In this paper, we attempt to revisit the behaviour of HITS from a different point of view. Namely, a similarity-based analysis model is applied to observing the distillation procedure. By defining a generalized similarity, an algorithm is proposed, which can improve the quality of distillation using only hyperlinks. A topic exploration function is also integrated into the algorithm framework, which enables end-users to search less popular topics when multi-topics are involved in queries. The experimental results reveal two benefits from the new algorithm: the improvement of distillation quality without utilizing any content information of pages, and an additional ability to explore the topics emerging in the query results.