Learning to rank under tight budget constraints

Authors:
Christian Pölitz;Ralf Schenkel
Affiliations:
Saarland University, Saarbrückeen, Germany;Saarland University, Saarbrücken, Germany
Venue:
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Year:
2011

Citing 5
Cited 0

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Abstract

This paper investigates the influence of pruning feature lists to keep a given budget for the evaluation of ranking methods. We learn from a given training set how important the individual prefixes are for the ranking quality. Based on there importance we choose the best prefixes to calculate the ranking while keeping the budget.