Query Chains: Learning to Rank from Implicit Feedback

Query Chains: Learning to Rank from Implicit Feedback (2005)

by Filip Radlinski , Thorsten Joachims

Citations:

119 - 8 self

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Datum	Value	Source
TITLE	Query Chains: Learning to Rank from Implicit Feedback	user correction - Legacy Corrections
AUTHOR NAME	Filip Radlinski	user correction - Legacy Corrections
AUTHOR AFFIL	; Department of Computer Science; Cornell University; Categories and Subject Descriptors; Search and Retrieval	user correction - Legacy Corrections
AUTHOR ADDR	; Ithaca, NY, USA	user correction - Legacy Corrections
AUTHOR NAME	Thorsten Joachims	user correction - Legacy Corrections
ABSTRACT	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.	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	239--248	INFERENCE
CITATIONS	25 found	ParsCit 1.0