BibSLEIGH — Donald

Travelled to:

1 × Brazil
1 × Canada
1 × China
1 × Germany
1 × Italy
1 × Portugal
1 × Switzerland
1 × The Netherlands
3 × United Kingdom
5 × USA

Collaborated with:

W.B.Croft ∅ J.J.Lin L.Wang F.Díaz T.Elsayed N.Asadi O.Kurland Y.Bernstein A.Moffat J.Zobel M.Bendersky S.Amer-Yahia T.Strohman S.T.Dumais C.Meek V.Lavrenko A.Z.Broder E.Gabrilovich V.Josifovski H.Lang B.Wang J.Li R.Jones F.Peng R.Zhang J.Novak H.Cui S.Reddy G.Mavromatis J.Wang M.Ciaramita M.Fontoura V.Murdock V.Plachouras

Talks about:

model (9) rank (7) retriev (6) inform (5) use (5) random (4) markov (4) improv (4) relev (4) field (4)

Person: Donald Metzler

DBLP: Metzler:Donald

Contributed to:

2012

2011

2010

2009

2008

2007

2006

2005

2004

Wrote 26 papers:

SIGIR-2012-MetzlerK #information retrieval: Experimental methods for information retrieval (DM, OK), pp. 1185–1186.
CIKM-2011-ElsayedLM #approximate #performance #retrieval: When close enough is good enough: approximate positional indexes for efficient ranked retrieval (TE, JJL, DM), pp. 1993–1996.
SIGIR-2011-AsadiMEL #learning #pseudo #ranking #web: Pseudo test collections for learning web search ranking functions (NA, DM, TE, JJL), pp. 1073–1082.
SIGIR-2011-AsadiML: Cross-corpus relevance projection (NA, DM, JJL), pp. 1163–1164.
SIGIR-2011-BenderskyMC #concept #query: Parameterized concept weighting in verbose queries (MB, DM, WBC), pp. 605–614.
SIGIR-2011-WangLM #performance #ranking #retrieval: A cascade ranking model for efficient ranked retrieval (LW, JJL, DM), pp. 105–114.
CIKM-2010-BroderGJMMW #web: Exploiting site-level information to improve web search (AZB, EG, VJ, GM, DM, JW), pp. 1393–1396.
CIKM-2010-LangMWL #concept #markov #random #using: Improved latent concept expansion using hierarchical markov random fields (HL, DM, BW, JTL), pp. 249–258.
CIKM-2010-WangML #constraints #ranking: Ranking under temporal constraints (LW, DM, JJL), pp. 79–88.
SIGIR-2010-DiazMA #online #ranking: Relevance and ranking in online dating systems (FD, DM, SAY), pp. 66–73.
SIGIR-2010-WangLM #learning #rank: Learning to efficiently rank (LW, JJL, DM), pp. 138–145.
SIGIR-2009-MetzlerJPZ #query: Improving search relevance for implicitly temporal queries (DM, RJ, FP, RZ), pp. 700–701.
SIGIR-2009-MetzlerNCR #documentation #using: Building enriched document representations using aggregated anchor text (DM, JN, HC, SR), pp. 219–226.
CIKM-2008-BroderCFGJMMP #learning: To swing or not to swing: learning when (not) to advertise (AZB, MC, MF, EG, VJ, DM, VM, VP), pp. 1003–1012.
CIKM-2008-Metzler #documentation: Generalized inverse document frequency (DM), pp. 399–408.
ECIR-2008-MetzlerSC #modelling #retrieval #statistics: A Statistical View of Binned Retrieval Models (DM, TS, WBC), pp. 175–186.
CIKM-2007-Metzler #automation #feature model #information retrieval #markov #random: Automatic feature selection in the markov random field model for information retrieval (DM), pp. 253–262.
ECIR-2007-MetzlerDM #metric #similarity: Similarity Measures for Short Segments of Text (DM, STD, CM), pp. 16–27.
SIGIR-2007-Metzler #modelling #parametricity #using: Using gradient descent to optimize language modeling smoothing parameters (DM), pp. 687–688.
SIGIR-2007-MetzlerC #concept #markov #random #using: Latent concept expansion using markov random fields (DM, WBC), pp. 311–318.
CIKM-2006-Metzler #estimation #modelling #retrieval: Estimation, sensitivity, and generalization in parameterized retrieval models (DM), pp. 812–813.
SIGIR-2006-DiazM #corpus #estimation #modelling #scalability #using: Improving the estimation of relevance models using large external corpora (FD, DM), pp. 154–161.
CIKM-2005-MetzlerBCMZ #data flow #metric #similarity: Similarity measures for tracking information flow (DM, YB, WBC, AM, JZ), pp. 517–524.
SIGIR-2005-MetzlerBCMZ #data flow #identification: The recap system for identifying information flow (DM, YB, WBC, AM, JZ), p. 678.
SIGIR-2005-MetzlerC #dependence #markov #random: A Markov random field model for term dependencies (DM, WBC), pp. 472–479.
SIGIR-2004-MetzlerLC #modelling #multi: Formal multiple-bernoulli models for language modeling (DM, VL, WBC), pp. 540–541.