Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Nardini F. M., Orlando S., Perego R., Tonellotto N. Speeding up document ranking with rank-based features. In: SIGIR '15 - 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (Santiago, Chile, 9-13 August 2015). Proceedings, pp. 895 - 898. ACM, 2015.
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%.
URL: http://dl.acm.org/citation.cfm?id=2767776&CFID=736403298&CFTOKEN=83169121
DOI: 10.1145/2766462.2767776
Subject Learning to Rank
H.3.3 Information Search and Retrieval. Search process

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