|
Published Articles >> Table of Contents >> Abstract
August 2007 (Vol. 40, No. 8)
pp. 34-40
Search Engines that Learn from Implicit Feedback
Thorsten Joachims, Cornell University
Filip Radlinski, Cornell University
Full Article Text:
   
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MC.2007.289
Send link to a friend
| 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.
|
 References
|
[1] J. Teevan, S.T. Dumais, and E. Horvitz, "Characterizing the Value of Personalizing Search," to be published in Proc. ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 07), ACM Press, 2007.
[2] F. Radlinski and T. Joachims, "Query Chains: Learning to Rank from Implicit Feedback," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 05), ACM Press, 2005, pp. 239–248.
[3] T. Joachims et al., "Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search," ACM Trans. Information Systems, vol. 25, no. 2, article 7, 2007.
[4] F. Radlinski and T. Joachims, "Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs," Proc. Nat'l Conf. Am. Assoc. for Artificial Intelligence (AAAI 06), AAAI, 2006, pp. 1406–1412.
[5] F. Radlinski and T. Joachims, "Active Exploration for Learning Rankings from Clickthrough Data," to be published in Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 07), ACM Press, 2007.
[6] T. Joachims, "Optimizing Search Engines Using Clickthrough Data," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 02), ACM Press, 2002, pp. 132–142.
[7] D. Kelly and J. Teevan, "Implicit Feedback for Inferring User Preference: A Bibliography," ACM SIGIR Forum, vol. 37, no. 2, 2003, pp. 18–28.
[8] E. Agichtein, E. Brill, and S. Dumais, "Improving Web Search Ranking by Incorporating User Behavior," Proc. ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 06), ACM Press, 2006, pp. 19–26.
[9] G. Furnas, "Experience with an Adaptive Indexing Scheme," Proc. ACM SIGCHI Conf. Human Factors in Computing Systems (CHI 85), ACM Press, 1985, pp. 131–135.
[10] W.W. Cohen, R.E. Shapire, and Y. Singer, "Learning to Order Things," J. Artificial Intelligence Research, vol. 10, AI Access Foundation, Jan.–June 1999, pp. 243–270.
[11] V. Vapnik, Statistical Learning Theory, John Wiley & Sons, 1998.
[12] R. Herbrich, T. Graepel, and K. Obermayer, "Large-Margin Rank Boundaries for Ordinal Regression," P. Bartlett et al., eds., Advances in Large-Margin Classifiers, MIT Press, 2000, pp. 115–132.
|
 Additional Information
|
Index Terms- search, pairwise preferences, Osmot engine, machine learning
Citation:
Thorsten Joachims, Filip Radlinski,
"Search Engines that Learn from Implicit Feedback,"
Computer,
vol. 40,
no. 8,
pp. 34-40,
Aug.,
2007
|
|
RSS Feed