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Time-based query performance predictors
Author(s) -
Nattiya Kanhabua,
Kjetil Nørvåg
Publication year - 2011
Publication title -
proceedings of the 45th international acm sigir conference on research and development in information retrieval
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2009916.2010109
Subject(s) - ranking (information retrieval) , computer science , relevance (law) , dimension (graph theory) , data mining , regression , query optimization , machine learning , query expansion , artificial neural network , information retrieval , artificial intelligence , statistics , mathematics , political science , pure mathematics , law
Query performance prediction is aimed at predicting the retrieval effectiveness that a query will achieve with respect to a particular ranking model. In this paper, we study query performance prediction for a ranking model that explicitly incorporates the time dimension into ranking. Different time-based predictors are proposed as analogous to existing keyword-based predictors. In order to improve predicting performance, we combine different predictors using linear regression and neural networks. Extensive experiments are conducted using queries and relevance judgments obtained by crowdsourcing.

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