Search Result Re-ranking Based on Gap between Search Queries and Social Tags

Presented at: 18th International World Wide Web Conference (WWW2009)

by Jun Yan, Ning Liu, Elaine Qing Chang, Lei Ji, Zheng Chen


Both search engine click-through log and social annotation have been utilized as user feedback for search result re-ranking. However, to our best knowledge, no previous study has explored the correlation between these two factors for the task of search result re-ranking. In this paper, we show that the gap between search queries and social tags of the same web page can well reflect its user preference score. Motivated by this observation, we propose a novel algorithm, called Query-Tag-Gap (QTG), to rerank search results for better user satisfaction. Intuitively, on one hand, the search users' intentions are generally described by their queries before they read the search results. On the other hand, the web annotators semantically tag web pages after they read the content of the pages. The difference between users' recognition of the same page before and after they read it is a good reflection of user satisfaction. In this extended abstract, we formally define the query set and tag set of the same page as users' pre- and postknowledge respectively. We empirically show the strong correlation between user satisfaction and user's knowledge gap before and after reading the page. Based on this gap, experiments have shown outstanding performance of our proposed QTG algorithm in search result re-ranking.

Keywords: Poster Session

Resource URI on the dog food server:

Explore this resource elsewhere: