@inproceedings{c48a06b509574c77a406bcaa1bdbdd68,
title = "Weight-based boosting model for cross-domain relevance ranking adaptation",
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.",
author = "Peng Cai and Wei Gao and Wong, \{Kam Fai\} and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2011.; 33rd European Conference on Information Retrieval, ECIR 2011 ; Conference date: 18-04-2011 Through 21-04-2011",
year = "2011",
doi = "10.1007/978-3-642-20161-5\_56",
language = "英语",
isbn = "9783642201608",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "562--567",
editor = "Paul Clough and Colum Foley and Cathal Gurrin and Hyowon Lee and Jones, \{Gareth J.F.\} and Wessel Kraaij and Vanessa Murdoch",
booktitle = "Advances in Information Retrieval - 33rd European Conference on IR Research, ECIR 2011, Proceedings",
address = "德国",
}