Chao Liu, Mei Li, and Yi-Min Wang
No search engine is perfect. A typical type of imperfection is the preference misalignment between search engines and end users, e.g., from time to time, web users skip higherranked documents and click on lower-ranked ones. Although search engines have been aggressively incorporating clickthrough data in their ranking, it is hard to eliminate such misalignments across millions of queries. Therefore, we, in this paper, propose to accompany a search engine with an “always-on” component that reorders documents on a perquery basis, based on user click patterns. Because of positional bias and dependencies between clicks, we show that a simple sort based on click counts (and its variants), albeit intuitive and useful, is not precise enough. In this paper, we put forward a principled approach to reordering documents by leveraging existing click models. Specifically, we compute the preference probability that a lower-ranked document is preferred to a higher-ranked one from the Click Chain Model (CCM), and propose to swap the two documents if the probability is sufficiently high. Because CCM models positional bias and dependencies between clicks, this method readily accounts for many twisted heuristics that have to be manually encoded in sort-based approaches. For this approach to be practical, we further devise two approximation schemes that make online computation of the preference probability feasible. We carried out a set of experiments based on real-world data from a major search engine, and the result clearly demonstrates the effectiveness of the proposed approach.
|Published in||CIKM '09: Proceedings of The 18th ACM Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery, Inc.|
Copyright © 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or firstname.lastname@example.org. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.