BibSLEIGH — Luo

Travelled to:

1 × Australia
1 × Brazil
1 × Canada
1 × Chile
1 × Finland
1 × Germany
1 × Ireland
1 × Israel
1 × Singapore
1 × Switzerland
1 × The Netherlands
1 × United Kingdom
11 × USA
3 × China

Collaborated with:

R.Jin J.P.Callan D.Zhang Q.Wang D.Hong S.Cetintas Y.Fang Z.Zhang J.Callan J.Y.Chai Y.Liu J.Ko A.P.Mathur H.Yang M.Wang B.Shen J.He R.D.Lawrence N.Zhang Y.Zhang X.Yuan S.Cooper Y.J.Nam C.He F.Wang J.Wang H.Yuan W.Jiang J.Li E.Nyberg L.Ruan L.Cen E.C.Dragut M.Ouzzani M.Rogati Y.P.Xin C.Hord J.Montgomery D.A.Evans C.Zhai P.Ogilvie A.G.Hauptmann D.Chen Z.Datbayev N.Somasundaram P.Bracke M.Witt T.Juchcinski Y.Lu X.Quan L.Dai J.Wang D.Song L.Liao C.Lin

Talks about:

model (18) search (14) select (7) result (7) inform (7) filter (7) learn (7) resourc (6) feder (6) hash (6)

Person: Luo Si

DBLP: Si:Luo

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2005

2004

2003

2002

2001

Wrote 47 papers:

SIGIR-2015-WangSWZSLL #cumulative #recommendation: An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation (JW, DS, QW, ZZ, LS, LL, CYL), pp. 635–644.
CIKM-2014-WangSZS #performance #scalability #semantics #similarity: Sparse Semantic Hashing for Efficient Large Scale Similarity Search (QW, BS, ZZ, LS), pp. 1899–1902.
SIGIR-2014-SiY #information retrieval #privacy #security: Privacy-preserving IR: when information retrieval meets privacy and security (LS, HY), p. 1295.
SIGIR-2014-WangSZZ: Active hashing with joint data example and tag selection (QW, LS, ZZ, NZ), pp. 405–414.
SIGIR-2014-ZhangWRS #performance #recommendation: Preference preserving hashing for efficient recommendation (ZZ, QW, LR, LS), pp. 183–192.
SIGIR-2014-ZhangZSLWY #online: Cross-domain and cross-category emotion tagging for comments of online news (YZ, NZ, LS, YL, QW, XY), pp. 627–636.
CIKM-2013-WangZS #performance #scalability #similarity: Weighted hashing for fast large scale similarity search (QW, DZ, LS), pp. 1185–1188.
ICML-c3-2013-ZhangHSL #learning #multi #named: MILEAGE: Multiple Instance LEArning with Global Embedding (DZ, JH, LS, RDL), pp. 82–90.
SIGIR-2013-CenDSO #adaptation #ambiguity #clustering: Author disambiguation by hierarchical agglomerative clustering with adaptive stopping criterion (LC, ECD, LS, MO), pp. 741–744.
SIGIR-2013-HongS: Search result diversification in resource selection for federated search (DH, LS), pp. 613–622.
SIGIR-2013-WangZS #modelling #semantics #topic #using: Semantic hashing using tags and topic modeling (QW, DZ, LS), pp. 213–222.
CIKM-2012-FangS #approach #feedback #learning #recommendation: A latent pairwise preference learning approach for recommendation from implicit feedback (YF, LS), pp. 2567–2570.
ITiCSE-2012-CooperNS #using: Initial results of using an intelligent tutoring system with Alice (SC, YJN, LS), pp. 138–143.
SIGIR-2012-HongS #algorithm #multi #retrieval: Mixture model with multiple centralized retrieval algorithms for result merging in federated search (DH, LS), pp. 821–830.
SIGIR-2012-ZhangFQDSY #classification #online: Emotion tagging for comments of online news by meta classification with heterogeneous information sources (YZ, YF, XQ, LD, LS, XY), pp. 1059–1060.
KDD-2011-ZhangHLSL #approach #learning #multi #scalability: Multi-view transfer learning with a large margin approach (DZ, JH, YL, LS, RDL), pp. 1208–1216.
KDD-2011-ZhangLS #learning: Serendipitous learning: learning beyond the predefined label space (DZ, YL, LS), pp. 1343–1351.
SIGIR-2011-CetintasCSSD #modelling #online #probability: Forecasting counts of user visits for online display advertising with probabilistic latent class models (SC, DC, LS, BS, ZD), pp. 1217–1218.
SIGIR-2011-CetintasRSF #identification #modelling #network #people #probability #social: Identifying similar people in professional social networks with discriminative probabilistic models (SC, MR, LS, YF), pp. 1209–1210.
SIGIR-2011-FangSSKM #analysis #query: Analysis of an expert search query log (YF, NS, LS, JK, APM), pp. 1189–1190.
SIGIR-2011-HeHS: A weighted curve fitting method for result merging in federated search (CH, DH, LS), pp. 1177–1178.
SIGIR-2011-SiJ #information retrieval #machine learning: Machine learning for information retrieval (LS, RJ), pp. 1293–1294.
SIGIR-2011-ZhangWS #multi: Composite hashing with multiple information sources (DZ, FW, LS), pp. 225–234.
SIGIR-2011-ZhangWS11a #clustering #documentation: Document clustering with universum (DZ, JW, LS), pp. 873–882.
SIGIR-2010-FangSM #documentation #modelling: Discriminative models of integrating document evidence and document-candidate associations for expert search (YF, LS, APM), pp. 683–690.
SIGIR-2010-HongSBWJ #classification #probability: A joint probabilistic classification model for resource selection (DH, LS, PB, MW, TJ), pp. 98–105.
CIKM-2009-CetintasSY #learning #query: Learning from past queries for resource selection (SC, LS, HY), pp. 1867–1870.
EDM-2009-CetintasSXH #correctness #low level #predict #problem: Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems (SC, LS, YPX, CH), pp. 230–239.
SIGIR-2009-ZhangS #modelling: Modeling search response time (DZ, LS), pp. 730–731.
SIGIR-2008-WangS #modelling #probability #retrieval: Discriminative probabilistic models for passage based retrieval (MW, LS), pp. 419–426.
SIGIR-2007-CetintasS #effectiveness #performance #trade-off: Exploration of the tradeoff between effectiveness and efficiency for results merging in federated search (SC, LS), pp. 707–708.
SIGIR-2007-JiangSL #privacy: Protecting source privacy in federated search (WJ, LS, JL), pp. 761–762.
SIGIR-2007-KoNS #probability #ranking #visual notation: A probabilistic graphical model for joint answer ranking in question answering (JK, EN, LS), pp. 343–350.
ICML-2005-JinCS #information retrieval #using: Learn to weight terms in information retrieval using category information (RJ, JYC, LS), pp. 353–360.
SIGIR-2005-SiC #effectiveness #modelling: Modeling search engine effectiveness for federated search (LS, JC), pp. 83–90.
CIKM-2004-SiC #framework: Unified utility maximization framework for resource selection (LS, JPC), pp. 32–41.
CIKM-2004-SiJ #collaboration #exponential: Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model (LS, RJ), pp. 156–157.
SIGIR-2004-JinCS #automation #collaboration: An automatic weighting scheme for collaborative filtering (RJ, JYC, LS), pp. 337–344.
SIGIR-2004-JinS #case study #collaboration #normalisation: A study of methods for normalizing user ratings in collaborative filtering (RJ, LS), pp. 568–569.
SIGIR-2004-MontgomerySCE #analysis #documentation #empirical #feedback: Effect of varying number of documents in blind feedback: analysis of the 2003 NRRC RIA workshop “bf_numdocs” experiment suite (JM, LS, JC, DAE), pp. 476–477.
CIKM-2003-JinSZC #collaboration #modelling: Collaborative filtering with decoupled models for preferences and ratings (RJ, LS, CZ, JPC), pp. 309–316.
ICML-2003-SiJ #collaboration #flexibility: Flexible Mixture Model for Collaborative Filtering (LS, RJ), pp. 704–711.
SIGIR-2003-SiC #documentation #estimation: Relevant document distribution estimation method for resource selection (LS, JPC), pp. 298–305.
CIKM-2002-SiJCO #framework #modelling: A language modeling framework for resource selection and results merging (LS, RJ, JPC, PO), pp. 391–397.
SIGIR-2002-JinSHC #information retrieval #using: Language model for IR using collection information (RJ, LS, AGH, JPC), pp. 419–420.
SIGIR-2002-SiC #using: Using sampled data and regression to merge search engine results (LS, JPC), pp. 19–26.
CIKM-2001-SiC #readability #statistics: A Statistical Model for Scientific Readability (LS, JPC), pp. 574–576.