document: Search result re-ranking based on gap between search queries and social tags 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 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 re-rank 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 post - knowledge 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 .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O