BibSLEIGH — Evgeniy

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: Gabrilovich:Evgeniy

Contributed to:

2015

2014

2012

2011

2010

2009

2008

2007

2004

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.