Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2011
Abstract
Adaptation techniques based on importance weighting were shown effective for RankSVM and RankNet, viz., each training instance is assigned a target weight denoting its importance to the target domain and incorporated into loss functions. In this work, we extend RankBoost using importance weighting framework for ranking adaptation. We find it non-trivial to incorporate the target weight into the boosting-based ranking algorithms because it plays a contradictory role against the innate weight of boosting, namely source weight that focuses on adjusting source-domain ranking accuracy. Our experiments show that among three variants, the additive weight-based RankBoost, which dynamically balances the two types of weights, significantly and consistently outperforms the baseline trained directly on the source domain.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 33rd European Conference on Information Retrieval (ECIR 2011)
First Page
562
Last Page
567
Identifier
10.1007/978-3-642-20161-5_56
Publisher
LNCS, Springer
City or Country
Dublin, Ireland
Citation
CAI, Peng; GAO, Wei; WONG, Kam-Fai; and ZHOU, Aoying.
Weight-based boosting model for cross-domain relevance ranking adaptation. (2011). Proceedings of the 33rd European Conference on Information Retrieval (ECIR 2011). 562-567.
Available at: https://ink.library.smu.edu.sg/sis_research/4596
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-642-20161-5_56