Search Engines that Learn from Implicit Feedback
Authors:
- 20 August 2007
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Computer
- Vol. 40 (8) , 34-40
Abstract
Search-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular user group or collection.Keywords
This publication has 10 references indexed in Scilit:
- Active exploration for learning rankings from clickthrough data2007
- Characterizing the value of personalizing search2007
- Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web searchACM Transactions on Information Systems, 2007
- Improving web search ranking by incorporating user behavior information2006
- Query chains2005
- Implicit feedback for inferring user preferenceACM SIGIR Forum, 2003
- Optimizing search engines using clickthrough data2002
- Large Margin Rank Boundaries for Ordinal Regression2000
- Learning to Order ThingsJournal of Artificial Intelligence Research, 1999
- Experience with an adaptive indexing scheme1985