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Travelled to:
1 × Canada
1 × Switzerland
1 × The Netherlands
2 × China
3 × USA
Collaborated with:
H.Zha Y.Chang G.Sun S.Ji C.Liao F.Li X.Li B.Long H.Fu A.Dong S.Yang T.Moon O.Chapelle B.L.Tseng Z.Kou J.Bian K.Zhou G.Xue M.Wu J.Ye J.Chow J.Chen K.Chen A.J.Smola S.Vadrevu G.Dupret L.Li W.Chu Y.Zhang J.Bai
Talks about:
rank (14) learn (10) function (5) web (5) user (4) use (4) incorpor (3) retriev (3) search (3) judgment (2)

Person: Zhaohui Zheng

DBLP DBLP: Zheng:Zhaohui

Contributed to:

SIGIR 20112011
CIKM 20102010
SIGIR 20102010
CIKM 20092009
SIGIR 20092009
SIGIR 20072007
CIKM 20062006
SIGIR 20062006

Wrote 17 papers:

SIGIR-2011-YangLSZZ #collaboration #learning #recommendation #using
Collaborative competitive filtering: learning recommender using context of user choice (SHY, BL, AJS, HZ, ZZ), pp. 295–304.
CIKM-2010-KouCZZ #learning #ranking
Learning to blend rankings: a monotonic transformation to blend rankings from heterogeneous domains (ZK, YC, ZZ, HZ), pp. 1921–1924.
CIKM-2010-LiLBZ #optimisation #ranking #web
Optimizing unified loss for web ranking specialization (FL, XL, JB, ZZ), pp. 1593–1596.
CIKM-2010-LongCVYZ #ranking
Ranking with auxiliary data (BL, YC, SV, SHY, ZZ), pp. 1489–1492.
CIKM-2010-MoonDJLZ #behaviour #ranking
User behavior driven ranking without editorial judgments (TM, GD, SJ, CL, ZZ), pp. 1473–1476.
CIKM-2010-MoonLCLZC #feedback #learning #online #ranking #realtime #using
Online learning for recency search ranking using real-time user feedback (TM, LL, WC, CL, ZZ, YC), pp. 1501–1504.
SIGIR-2010-LongCZCZT #learning #optimisation #ranking
Active learning for ranking through expected loss optimization (BL, OC, YZ, YC, ZZ, BLT), pp. 267–274.
CIKM-2009-BaiZXZSTZC #learning #multi #rank #web
Multi-task learning for learning to rank in web search (JB, KZ, GRX, HZ, GS, BLT, ZZ, YC), pp. 1549–1552.
CIKM-2009-LiLJZCD #evaluation #ranking #robust #web
Incorporating robustness into web ranking evaluation (XL, FL, SJ, ZZ, YC, AD), pp. 2007–2010.
CIKM-2009-WuCZZ #approach #definite clause grammar #learning #novel #rank #using
Smoothing DCG for learning to rank: a novel approach using smoothed hinge functions (MW, YC, ZZ, HZ), pp. 1923–1926.
CIKM-2009-YeCCZ #distributed #probability
Stochastic gradient boosted distributed decision trees (JY, JHC, JC, ZZ), pp. 2061–2064.
SIGIR-2009-ChangDLZ #ranking #topic
Enhancing topical ranking with preferences from click-through data (YC, AD, CL, ZZ), pp. 666–667.
SIGIR-2009-JiZLZXCSZ #ranking
Global ranking by exploiting user clicks (SJ, KZ, CL, ZZ, GRX, OC, GS, HZ), pp. 35–42.
SIGIR-2009-LiLJZ #ranking #robust #web
Comparing both relevance and robustness in selection of web ranking functions (FL, XL, SJ, ZZ), pp. 648–649.
SIGIR-2007-ZhengCSZ #framework #learning #ranking #using
A regression framework for learning ranking functions using relative relevance judgments (ZZ, KC, GS, HZ), pp. 287–294.
CIKM-2006-ZhaZFS #difference #learning #query #retrieval #web
Incorporating query difference for learning retrieval functions in world wide web search (HZ, ZZ, HF, GS), pp. 307–316.
SIGIR-2006-ZhaZFS #difference #information retrieval #learning #query
Incorporating query difference for learning retrieval functions in information retrieval (HZ, ZZ, HF, GS), pp. 721–722.

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