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Travelled to:
1 × Ireland
1 × Portugal
1 × Singapore
1 × Switzerland
1 × The Netherlands
2 × Canada
2 × United Kingdom
3 × China
3 × USA
Collaborated with:
V.Josifovski A.Z.Broder E.Agichtein S.Markovitch L.Riedel A.Bordes M.Fontoura H.Becker B.Pang G.Dror K.Murphy W.Horn S.Sun W.Zhang P.Ciccolo D.Davidov D.Metzler X.L.Dong G.Heitz G.Halawi Y.Koren S.K.Tyler S.Pandey A.Aji Y.Wang D.Agarwal R.Hall R.Khanna A.Anagnostopoulos G.Mavromatis J.Wang F.Radlinski A.Joshi T.Zhang Q.Liu Y.Maarek D.Pelleg I.Szpektor V.Dang C.Lugaresi M.Ciaramita V.Murdock V.Plachouras X.Dong N.Lao T.Strohmann
Talks about:
advertis (10) web (8) knowledg (7) retriev (5) search (4) inform (4) base (4) use (4) approach (3) fusion (3)

Person: Evgeniy Gabrilovich

DBLP DBLP: Gabrilovich:Evgeniy

Contributed to:

VLDB 20152015
KDD 20142014
VLDB 20142014
KDD 20122012
CIKM 20112011
ECIR 20112011
SIGIR 20112011
CIKM 20102010
SIGIR 20102010
CIKM 20092009
SIGIR 20092009
CIKM 20082008
SIGIR 20082008
CIKM 20072007
SIGIR 20072007
ICML 20042004
SIGIR 20042004

Wrote 23 papers:

VLDB-2015-DongGMDHLSZ #knowledge-based #trust #web
Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources (XLD, EG, KM, VD, WH, CL, SS, WZ), pp. 938–949.
KDD-2014-0001GHHLMSSZ #approach #probability
Knowledge vault: a web-scale approach to probabilistic knowledge fusion (XD, EG, GH, WH, NL, KM, TS, SS, WZ), pp. 601–610.
KDD-2014-BordesG #graph #mining #tutorial
Constructing and mining web-scale knowledge graphs: KDD 2014 tutorial (AB, EG), p. 1967.
VLDB-2014-DongGHHMSZ #data fusion
From Data Fusion to Knowledge Fusion (XLD, EG, GH, WH, KM, SS, WZ), pp. 881–892.
KDD-2012-HalawiDGK #constraints #learning #scalability #word
Large-scale learning of word relatedness with constraints (GH, GD, EG, YK), pp. 1406–1414.
CIKM-2011-BroderGJ #challenge #information retrieval
Information retrieval challenges in computational advertising (AZB, EG, VJ), pp. 2611–2612.
CIKM-2011-TylerPGJ #modelling #retrieval
Retrieval models for audience selection in display advertising (SKT, SP, EG, VJ), pp. 593–598.
ECIR-2011-Gabrilovich #knowledge-based #retrieval
Ad Retrieval Systems in vitro and in vivo: Knowledge-Based Approaches to Computational Advertising (EG), pp. 4–5.
SIGIR-2011-AgichteinG #information management #retrieval
Information organization and retrieval with collaboratively generated content (EA, EG), pp. 1307–1308.
SIGIR-2011-LiuADGMPS #predict #web
Predicting web searcher satisfaction with existing community-based answers (QL, EA, GD, EG, YM, DP, IS), pp. 415–424.
CIKM-2010-AjiWAG #analysis #modelling #using
Using the past to score the present: extending term weighting models through revision history analysis (AA, YW, EA, EG), pp. 629–638.
CIKM-2010-BroderGJMMW #web
Exploiting site-level information to improve web search (AZB, EG, VJ, GM, DM, JW), pp. 1393–1396.
SIGIR-2010-BroderGJ #challenge #information retrieval
Information retrieval challenges in computational advertising (AZB, EG, VJ), p. 908.
CIKM-2009-AgarwalGHJK
Translating relevance scores to probabilities for contextual advertising (DA, EG, RH, VJ, RK), pp. 1899–1902.
CIKM-2009-BeckerBGJP #web #what
What happens after an ad click?: quantifying the impact of landing pages in web advertising (HB, AZB, EG, VJ, BP), pp. 57–66.
SIGIR-2009-BeckerBGJP
Context transfer in search advertising (HB, AZB, EG, VJ, BP), pp. 656–657.
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-BroderCFGJR #feedback #using #web
Search advertising using web relevance feedback (AZB, PC, MF, EG, VJ, LR), pp. 1013–1022.
SIGIR-2008-RadlinskiBCGJR #approach #optimisation #query
Optimizing relevance and revenue in ad search: a query substitution approach (FR, AZB, PC, EG, VJ, LR), pp. 403–410.
CIKM-2007-AnagnostopoulosBGJR
Just-in-time contextual advertising (AA, AZB, EG, VJ, LR), pp. 331–340.
SIGIR-2007-BroderFGJJZ #classification #query #robust #using #web
Robust classification of rare queries using web knowledge (AZB, MF, EG, AJ, VJ, TZ), pp. 231–238.
ICML-2004-GabrilovichM #categorisation #feature model #using
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5 (EG, SM).
SIGIR-2004-DavidovGM #categorisation #dataset #generative
Parameterized generation of labeled datasets for text categorization based on a hierarchical directory (DD, EG, SM), pp. 250–257.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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