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.
This work is partially supported by NSFC grant (No. 60925008), 973 program (No. 2010CB731402) and 863 program (No. 2009AA01Z150) of China.
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Cai, P., Gao, W., Wong, KF., Zhou, A. (2011). Weight-Based Boosting Model for Cross-Domain Relevance Ranking Adaptation. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_56
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DOI: https://doi.org/10.1007/978-3-642-20161-5_56
Publisher Name: Springer, Berlin, Heidelberg
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