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2012 23rd International Workshop on Database and Expert Systems Applications (2012)
Vienna, Austria Austria
Sept. 3, 2012 to Sept. 7, 2012
ISSN: 1529-4188
ISBN: 978-1-4673-2621-6
pp: 161-165
ABSTRACT
This paper introduces a budget-aware learning to rank approach that limits the cost for evaluating a ranking model, with a focus on very tight budgets that do not allow to fully evaluate at least for one time all documents for each term. In contrast to existing work on budget-aware learning to rank, our model allows to only partially evaluate parts of the ranking model for the most promising documents. In contrast to existing work on top-k retrieval, we generate an execution plan before the actual query processing starts, eliminating the need for expensive in-memory accumulator management. We consider a unified cost model that integrates loading and processing cost. An extensive evaluation with a standard benchmark collection shows that our method outperforms other budget-aware methods under tight budgets in terms of result quality
INDEX TERMS
Mathematical model, Loading, Load modeling, Optimization, Computational modeling, Equations, Query processing, Constraints, Learning to Rank
CITATION
Christian Politz, Ralf Schenkel, "Ranking under Tight Budgets", 2012 23rd International Workshop on Database and Expert Systems Applications, vol. 00, no. , pp. 161-165, 2012, doi:10.1109/DEXA.2012.21
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