Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2010

Abstract

Like traditional supervised and semi-supervised algorithms, learning to rank for information retrieval requires document annotations provided by domain experts. It is costly to annotate training data for different search domains and tasks. We propose to exploit training data annotated for a related domain to learn to rank retrieved documents in the target domain, in which no labeled data is available. We present a simple yet effective approach based on instance-weighting scheme. Our method first estimates the importance of each related-domain document relative to the target domain. Then heuristics are studied to transform the importance of individual documents to the pairwise weights of document pairs, which can be directly incorporated into the popular ranking algorithms. Due to importance weighting, ranking model trained on related domain is highly adaptable to the data of target domain. Ranking adaptation experiments on LETOR3.0 dataset [27] demonstrate that with a fair amount of related-domain training data, our method significantly outperforms the baseline without weighting, and most of time is not significantly worse than an "ideal" model directly trained on target domain.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 33rd Annual International ACM SIGIR7 Conference on Research and Development in Information Retrieval (SIGIR 2010)

First Page

162

Last Page

169

Identifier

10.1145/1835449.1835478

Publisher

ACM Press

City or Country

Geneva, Switzerland

Additional URL

https://doi.org/10.1145/1835449.1835478

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