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Post-Rank Reordering: Resolving Preference Misalignments between Search Engines and End Users

Chao Liu, Mei Li, and Yi-Min Wang

Abstract

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.

Details

Publication typeInproceedings
Published inCIKM '09: Proceedings of The 18th ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc.
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