BibSLEIGH — Zhaohui

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: Zheng:Zhaohui

Contributed to:

2011

2010

2009

2007

2006

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.