Selective gradient boosting for effective learning to rank
Lucchese C., Nardini F. M., Perego R., Orlando S., Trani S.
Settore INF/01 - Informatica Learning-to-Rank task Multiple additive regression trees Learning to Rank Boosting Learning to rank Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni Web search production systems Multiple Additive Regression Trees NDCG
Learning an effective ranking function from a large number of query-document examples is a challenging task. Indeed, training sets where queries are associated with a few relevant documents and a large number of irrelevant ones are required to model real scenarios of Web search production systems, where a query can possibly retrieve thousands of matching documents, but only a few of them are actually relevant. In this paper, we propose Selective Gradient Boosting (SelGB), an algorithm addressing the Learning-to-Rank task by focusing on those irrelevant documents that are most likely to be mis-ranked, thus severely hindering the quality of the learned model. SelGB exploits a novel technique minimizing the mis-ranking risk, i.e., the probability that two randomly drawn instances are ranked incorrectly, within a gradient boosting process that iteratively generates an additive ensemble of decision trees. Specifically, at every iteration and on a per query basis, SelGB selectively chooses among the training instances a small sample of negative examples enhancing the discriminative power of the learned model. Reproducible and comprehensive experiments conducted on a publicly available dataset show that SelGB exploits the diversity and variety of the negative examples selected to train tree ensembles that outperform models generated by state-of-the-art algorithms by achieving improvements of NDCG@10 up to 3.2%.
Source: International ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 155–164, 8-12/07/2018
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@inproceedings{oai:it.cnr:prodotti:401220,
title = {Selective gradient boosting for effective learning to rank},
author = {Lucchese C. and Nardini F. M. and Perego R. and Orlando S. and Trani S.},
doi = {10.1145/3209978.3210048 and 10.5281/zenodo.2668014 and 10.5281/zenodo.2668013},
booktitle = {International ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 155–164, 8-12/07/2018},
year = {2018}
}
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