PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Tonellotto N., Macdonald C., Ounis I. Efficient and effective retrieval using selective pruning. In: WSDM 2013 - Sixth ACM International Conference on Web Search and Data Mining (Roma, Italy, 4-8 February 2013). Proceedings, pp. 63 - 72. ACM, 2013.
 
 
Abstract
(English)
Retrieval can be made more efficient by deploying dynamic pruning strategies such as Wand, which do not degrade effectiveness up to a given rank. It is possible to increase the efficiency of such techniques by pruning more 'aggressively'. However, this may reduce effectiveness. In this work, we propose a novel selective framework that determines the appropriate amount of pruning aggressiveness on a per-query basis, thereby increasing overall efficiency without significantly reducing overall effectiveness. We postulate two hypotheses about the queries that should be pruned more aggressively, which generate two approaches within our framework, based on query performance predictors and query efficiency predictors, respectively. We thoroughly experiment to ascertain the efficiency and effectiveness impacts of the proposed approaches, as part of a search engine deploying state-of-the-art learning to rank techniques. Our results on 50 million documents of the TREC ClueWeb09 collection show that by using query efficiency predictors to target inefficient queries, we observe that a 36% reduction in mean response time and a 50% reduction of the response times experienced by the slowest 10% of queries can be achieved while still ensuring effectiveness.
URL: http://dl.acm.org/citation.cfm?id=2433407
DOI: 10.1145/2433396.2433407
Subject Information Retrieval
H.3.3 Information Search and Retrieval


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