Document - Speeding up document ranking with rank-based features

2015

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Speeding up document ranking with rank-based features

Lucchese C., Nardini F. M., Orlando S., Perego R., Tonellotto N.

Efficiency Learning to Rank Meta-features

Learning to Rank (LtR) is an effective machine learning me- thodology for inducing high-quality document ranking func- tions. Given a query and a candidate set of documents, where query-document pairs are represented by feature vec- tors, a machine-learned function is used to reorder this set. In this paper we propose a new family of rank-based features, which extend the original feature vector associated with each query-document pair. Indeed, since they are derived as a function of the query-document pair and the full set of can- didate documents to score, rank-based features provide ad- ditional information to better rank documents and return the most relevant ones. We report a comprehensive evalu- ation showing that rank-based features allow us to achieve the desired effectiveness with ranking models being up to 3.5 times smaller than models not using them, with a scoring time reduction up to 70%.

Source: 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 895–898, Santiago, Chile, 9-13 August 2015

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@inproceedings{oai:it.cnr:prodotti:342591,
	title = {Speeding up document ranking with rank-based features},
	author = {Lucchese C. and Nardini F.  M. and Orlando S. and Perego R. and Tonellotto N.},
	doi = {10.1145/2766462.2767776},
	booktitle = {38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 895–898, Santiago, Chile, 9-13 August 2015},
	year = {2015}
}