BibSLEIGH — Qiaozhu

Travelled to:

1 × France
1 × Germany
1 × Singapore
1 × The Netherlands
2 × Australia
2 × China
9 × USA

Collaborated with:

C.Zhai J.Han J.Tang D.Xin X.Shen M.Zhang X.Rong C.Li M.Qu J.Guo D.R.Radev D.Zhou K.W.Church D.Zhang H.Fang Y.Wang P.Resnick C.X.Lin B.Zhao K.L.Klinkner R.Kumar A.Tomkins Z.Yin R.Li T.Wu D.Cai C.Zhai X.Ling B.R.Schatz T.Tao X.Wang Y.Lu D.Wang S.Pandey Z.Meng X.Nguyen S.Kong L.Feng F.Ye Z.Zhao Z.Guan J.Bu C.Chen C.Wang H.Cheng Z.Chen Y.Cao X.Lu X.Liu

Talks about:

model (9) text (6) analysi (5) topic (5) network (4) predict (3) pattern (3) social (3) queri (3) multi (3)

Person: Qiaozhu Mei

DBLP: Mei:Qiaozhu

Contributed to:

2015

2014

2013

2010

2009

2008

2007

2006

2005

2019

Wrote 25 papers:

KDD-2015-LiLMWP #predict #timeline #twitter: Click-through Prediction for Advertising in Twitter Timeline (CL, YL, QM, DW, SP), pp. 1959–1968.
KDD-2015-TangQM #named #network #predict #scalability: PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks (JT, MQ, QM), pp. 1165–1174.
ICML-c1-2014-TangMNMZ #analysis #comprehension #modelling #topic: Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis (JT, ZM, XN, QM, MZ), pp. 190–198.
SIGIR-2014-KongMFYZ #predict #realtime: Predicting bursts and popularity of hashtags in real-time (SK, QM, LF, FY, ZZ), pp. 927–930.
SIGIR-2014-LiWRM #classification #interactive #named #query #retrieval: ReQ-ReC: high recall retrieval with query pooling and interactive classification (CL, YW, PR, QM), pp. 163–172.
CIKM-2013-RongM #network #social: Diffusion of innovations revisited: from social network to innovation network (XR, QM), pp. 499–508.
KDD-2013-TangZM #modelling #multi #topic: One theme in all views: modeling consensus topics in multiple contexts (JT, MZ, QM), pp. 5–13.
KDD-2010-LinZMH #community #named #social #statistics: PET: a statistical model for popular events tracking in social communities (CXL, BZ, QM, JH), pp. 929–938.
KDD-2010-MeiGR #named #network: DivRank: the interplay of prestige and diversity in information networks (QM, JG, DRR), pp. 1009–1018.
CIKM-2009-MeiKKT #analysis #framework #sequence: An analysis framework for search sequences (QM, KLK, RK, AT), pp. 1991–1994.
KDD-2009-YinLMH #classification #graph #social #web: Exploring social tagging graph for web object classification (ZY, RL, QM, JH), pp. 957–966.
SIGIR-2009-GuanBMCW #graph #multi #personalisation #ranking #recommendation #using: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects (ZG, JB, QM, CC, CW), pp. 540–547.
VLDB-2009-WuXMH #analysis #multi: Promotion Analysis in Multi-Dimensional Space (TW, DX, QM, JH), pp. 109–120.
CIKM-2008-CaiMHZ #documentation #modelling #topic: Modeling hidden topics on document manifold (DC, QM, JH, CZ), pp. 911–920.
CIKM-2008-MeiZC #query #using: Query suggestion using hitting time (QM, DZ, KWC), pp. 469–478.
KDD-2008-LingMZS #mining #multi #topic: Mining multi-faceted overviews of arbitrary topics in a text collection (XL, QM, CZ, BRS), pp. 497–505.
SIGIR-2008-MeiZZ #framework #graph #modelling #optimisation: A general optimization framework for smoothing language models on graph structures (QM, DZ, CZ), pp. 611–618.
KDD-2007-MeiSZ #automation #modelling #multi #topic: Automatic labeling of multinomial topic models (QM, XS, CZ), pp. 490–499.
SIGIR-2007-MeiFZ #case study #generative #information retrieval #query: A study of Poisson query generation model for information retrieval (QM, HF, CZ), pp. 319–326.
KDD-2006-MeiXCHZ #analysis #generative #semantics: Generating semantic annotations for frequent patterns with context analysis (QM, DX, HC, JH, CZ), pp. 337–346.
KDD-2006-MeiZ #mining: A mixture model for contextual text mining (QM, CZ), pp. 649–655.
KDD-2006-XinSMH #feedback #interactive: Discovering interesting patterns through user’s interactive feedback (DX, XS, QM, JH), pp. 773–778.
CIKM-2005-TaoWMZ #documentation #estimation: Accurate language model estimation with document expansion (TT, XW, QM, CZ), pp. 273–274.
KDD-2005-MeiZ #mining: Discovering evolutionary theme patterns from text: an exploration of temporal text mining (QM, CZ), pp. 198–207.
ESEC-FSE-2019-ChenCLML #analysis #approach #learning #named #re-engineering #sentiment: SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering (ZC, YC, XL, QM, XL), pp. 841–852.