caa a22 4500 463251474 CHVBK 20180405153341.0 cr unu---uuuuu 170326e20070601xx s 000 0 eng 10.1007/s10791-007-9025-9 doi (NATIONALLICENCE)springer-10.1007/s10791-007-9025-9 On rank-based effectiveness measures and optimization [Elektronische Daten] [Stephen Robertson, Hugo Zaragoza] Many current retrieval models and scoring functions contain free parameters which need to be set—ideally, optimized. The process of optimization normally involves some training corpus of the usual document-query-relevance judgement type, and some choice of measure that is to be optimized. The paper proposes a way to think about the process of exploring the space of parameter values, and how moving around in this space might be expected to affect different measures. One result, concerning local optima, is demonstrated for a range of rank-based evaluation measures. Springer Science+Business Media, LLC, 2007 Effectiveness metrics nationallicence Ranking functions nationallicence Optimization nationallicence Robertson Stephen Microsoft Research, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, UK aut Zaragoza Hugo Yahoo! Research, Ocata 1, 08003, Barcelona, Spain aut Information Retrieval Kluwer Academic Publishers 10/3(2007-06-01), 321-339 1386-4564 10:3<321 2007 10 10791 https://doi.org/10.1007/s10791-007-9025-9 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10791-007-9025-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Robertson Stephen Microsoft Research, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, UK aut NATIONALLICENCE 700 1- Zaragoza Hugo Yahoo! Research, Ocata 1, 08003, Barcelona, Spain aut NATIONALLICENCE 773 0- Information Retrieval Kluwer Academic Publishers 10/3(2007-06-01), 321-339 1386-4564 10:3<321 2007 10 10791 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer