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Travelled to:
1 × Brazil
1 × Canada
1 × Chile
1 × Finland
1 × Ireland
1 × Italy
1 × Portugal
1 × Singapore
1 × Switzerland
1 × The Netherlands
1 × United Kingdom
3 × China
8 × USA
Collaborated with:
H.Li T.Qin B.Gao W.Ma Z.Ma T.Wang X.Zhang Y.Lan X.Geng X.Zheng M.Tsai J.Xu J.Bian Y.Bao K.Salomatin Y.Yang Y.Zhang J.Yan D.Shen S.Kim H.Yu Q.Zhao S.S.Bhowmick S.Liu Y.Zhang W.Wei F.Xia J.Wang W.Zhang M.Lu L.Yang L.Qi Y.Zhao H.Yang L.Zhang Z.Cao H.Chen Q.Cheng Z.Chen S.Huang S.Wang J.Ma Z.Chen J.Veijalainen W.Zhang Y.Yu X.Yuan A.Arnold H.Shum D.Wang W.Lai Y.Cao Y.Huang H.Hon C.Xu Y.Bai G.Wang X.Liu Y.Liu S.He G.Feng Y.Wang
Talks about:
rank (16) learn (7) search (5) web (5) approach (4) advertis (4) listwis (4) general (4) optim (4) graph (4)

Person: Tie-Yan Liu

DBLP DBLP: Liu:Tie=Yan

Contributed to:

SIGIR 20152015
CIKM 20142014
KDD 20132013
SIGIR 20132013
CIKM 20122012
KDD 20122012
SIGIR 20122012
CIKM 20112011
KDD 20112011
SIGIR 20102010
CIKM 20092009
ICML 20092009
ICML 20082008
SIGIR 20082008
CIKM 20072007
ECIR 20072007
ICML 20072007
SIGIR 20072007
KDD 20062006
SIGIR 20062006
KDD 20052005
SIGIR 20052005

Wrote 29 papers:

SIGIR-2015-HuangWLMCV #collaboration
Listwise Collaborative Filtering (SH, SW, TYL, JM, ZC, JV), pp. 343–352.
CIKM-2014-XuBBGWLL #framework #named #word
RC-NET: A General Framework for Incorporating Knowledge into Word Representations (CX, YB, JB, BG, GW, XL, TYL), pp. 1219–1228.
KDD-2013-WangBLZL #predict
Psychological advertising: exploring user psychology for click prediction in sponsored search (TW, JB, SL, YZ, TYL), pp. 563–571.
SIGIR-2013-GaoYSL #internet #theory and practice
Internet advertising: theory and practice (BG, JY, DS, TYL), p. 1135.
CIKM-2012-SalomatinLY #framework #online #optimisation
A unified optimization framework for auction and guaranteed delivery in online advertising (KS, TYL, YY), pp. 2005–2009.
KDD-2012-ZhangZGYYL #optimisation
Joint optimization of bid and budget allocation in sponsored search (WZ, YZ, BG, YY, XY, TYL), pp. 1177–1185.
SIGIR-2012-GaoWL #graph #information retrieval #learning #mining #scalability
Large-scale graph mining and learning for information retrieval (BG, TW, TYL), pp. 1194–1195.
CIKM-2011-KimQYL #approach #behaviour
Advertiser-centric approach to understand user click behavior in sponsored search (SK, TQ, HY, TYL), pp. 2121–2124.
KDD-2011-GaoLWWL #graph #metadata #ranking #scalability
Semi-supervised ranking on very large graphs with rich metadata (BG, TYL, WW, TW, HL), pp. 96–104.
SIGIR-2010-Liu #information retrieval #learning #rank
Learning to rank for information retrieval (TYL), p. 904.
CIKM-2009-GaoLMWL #framework #markov
A general markov framework for page importance computation (BG, TYL, ZM, TW, HL), pp. 1835–1838.
ICML-2009-LanLML #algorithm #analysis #ranking
Generalization analysis of listwise learning-to-rank algorithms (YL, TYL, ZM, HL), pp. 577–584.
ICML-2008-LanLQML #learning #rank
Query-level stability and generalization in learning to rank (YL, TYL, TQ, ZM, HL), pp. 512–519.
ICML-2008-XiaLWZL #algorithm #approach #learning #rank
Listwise approach to learning to rank: theory and algorithm (FX, TYL, JW, WZ, HL), pp. 1192–1199.
SIGIR-2008-GengLQALS #nearest neighbour #query #ranking #using
Query dependent ranking using K-nearest neighbor (XG, TYL, TQ, AA, HL, HYS), pp. 115–122.
SIGIR-2008-LiuGLZMHL #named #rank #web
BrowseRank: letting web users vote for page importance (YL, BG, TYL, YZ, ZM, SH, HL), pp. 451–458.
SIGIR-2008-XuLLLM #evaluation #learning #metric #optimisation #rank
Directly optimizing evaluation measures in learning to rank (JX, TYL, ML, HL, WYM), pp. 107–114.
CIKM-2007-YangQZGL #analysis #graph #using #web
Link analysis using time series of web graphs (LY, LQ, YPZ, BG, TYL), pp. 1011–1014.
ECIR-2007-LiuYZQM #clustering #optimisation #performance #scalability
Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization (TYL, HYY, XZ, TQ, WYM), pp. 319–330.
ECIR-2007-ZhangQLBL #rank #web
N -Step PageRank for Web Search (LZ, TQ, TYL, YB, HL), pp. 653–660.
ICML-2007-CaoQLTL #approach #learning #rank
Learning to rank: from pairwise approach to listwise approach (ZC, TQ, TYL, MFT, HL), pp. 129–136.
SIGIR-2007-GengLQL #feature model #ranking
Feature selection for ranking (XG, TYL, TQ, HL), pp. 407–414.
SIGIR-2007-QinZWLLL #multi #ranking
Ranking with multiple hyperplanes (TQ, XDZ, DSW, TYL, WL, HL), pp. 279–286.
SIGIR-2007-TsaiLQCM #named #ranking
FRank: a ranking method with fidelity loss (MFT, TYL, TQ, HHC, WYM), pp. 383–390.
KDD-2006-ZhaoLBM #detection #evolution
Event detection from evolution of click-through data (QZ, TYL, SSB, WYM), pp. 484–493.
SIGIR-2006-CaoXLLHH #adaptation #documentation #ranking #retrieval
Adapting ranking SVM to document retrieval (YC, JX, TYL, HL, YH, HWH), pp. 186–193.
SIGIR-2006-FengLWBMZM #named #order #web
AggregateRank: bringing order to web sites (GF, TYL, YW, YB, ZM, XDZ, WYM), pp. 75–82.
KDD-2005-GaoLZCM #clustering #consistency #graph #higher-order #semistructured data
Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering (BG, TYL, XZ, QC, WYM), pp. 41–50.
SIGIR-2005-QinLZCM #case study #web
A study of relevance propagation for web search (TQ, TYL, XDZ, ZC, WYM), pp. 408–415.

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