document: Query chains : learning to rank from implicit feedback This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results . We observe that users searching the web often perform a sequence , or chain , of queries with a similar information need . Using query chains , we generate new types of preference judgments from search engine logs , thus taking advantage of user intelligence in reformulating queries . To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments . We also implemented a real-world search engine to test our approach , using a modified ranking SVM to learn an improved ranking function from preference data . Our results demonstrate significant improvements in the ranking given by the search engine . The learned rankings outperform both a static ranking function , as well as one trained without considering query chains .
doc_bio_tags: O O O O O O O B I O O O O O O O O B 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