BibSLEIGH — Jiafeng

Travelled to:

1 × Australia
1 × Canada
1 × Chile
1 × Ireland
1 × Russia
1 × Singapore
1 × The Netherlands
1 × United Kingdom
2 × China
4 × USA

Collaborated with:

X.Cheng Y.Lan S.Niu G.Xu X.Zhu P.Du X.Yan P.Wang H.Li C.Fan J.Xu S.Wan L.Bai J.Zhang H.Shen L.Xia X.Geng Y.Zhu W.Nejdl Z.Lin S.Liu Y.Wang

Talks about:

recommend (8) queri (8) rank (7) model (6) learn (6) data (4) factor (3) relev (3) high (3) top (3)

Person: Jiafeng Guo

DBLP: Guo:Jiafeng

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

Wrote 21 papers:

SIGIR-2015-WangGLXWC #learning #recommendation #representation: Learning Hierarchical Representation Model for NextBasket Recommendation (PW, JG, YL, JX, SW, XC), pp. 403–412.
SIGIR-2015-XiaXLGC #evaluation #learning #metric #optimisation: Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures (LX, JX, YL, JG, XC), pp. 113–122.
CIKM-2014-WangGL #modelling #personalisation #recommendation #transaction: Modeling Retail Transaction Data for Personalized Shopping Recommendation (PW, JG, YL), pp. 1979–1982.
ECIR-2014-BaiGLC #documentation #linear #matrix #modelling: Local Linear Matrix Factorization for Document Modeling (LB, JG, YL, XC), pp. 398–411.
SIGIR-2014-NiuLGCG #data analysis #learning #rank #robust #what: What makes data robust: a data analysis in learning to rank (SN, YL, JG, XC, XG), pp. 1191–1194.
SIGIR-2014-ZhuLGCN #learning: Learning for search result diversification (YZ, YL, JG, XC, SN), pp. 293–302.
CIKM-2013-LanNGC #question #ranking: Is top-k sufficient for ranking? (YL, SN, JG, XC), pp. 1261–1270.
ECIR-2013-ZhuGCLN #graph #query #recommendation: Recommending High Utility Query via Session-Flow Graph (XZ, JG, XC, YL, WN), pp. 642–655.
SIGIR-2013-FanLGLC #collaboration #recommendation: Collaborative factorization for recommender systems (CF, YL, JG, ZL, XC), pp. 949–953.
SIGIR-2013-WanLGFC #recommendation #social #social media: Informational friend recommendation in social media (SW, YL, JG, CF, XC), pp. 1045–1048.
CIKM-2012-NiuLGC #probability #problem #ranking: A new probabilistic model for top-k ranking problem (SN, YL, JG, XC), pp. 2519–2522.
CIKM-2012-YanGLCW #clustering #matrix #using: Clustering short text using Ncut-weighted non-negative matrix factorization (XY, JG, SL, XC, YW), pp. 2259–2262.
CIKM-2012-ZhuGCL #behaviour #mining #query #recommendation: More than relevance: high utility query recommendation by mining users’ search behaviors (XZ, JG, XC, YL), pp. 1814–1818.
SIGIR-2012-NiuGLC #evaluation #learning #rank #ranking: Top-k learning to rank: labeling, ranking and evaluation (SN, JG, YL, XC), pp. 751–760.
CIKM-2011-GuoCXZ #query #similarity: Intent-aware query similarity (JG, XC, GX, XZ), pp. 259–268.
CIKM-2011-YanGC #higher-order #learning #query #recommendation: Context-aware query recommendation by learning high-order relation in query logs (XY, JG, XC), pp. 2073–2076.
SIGIR-2011-DuGC #network: Decayed DivRank: capturing relevance, diversity and prestige in information networks (PD, JG, XC), pp. 1239–1240.
CIKM-2010-DuGZC #ranking #summary: Manifold ranking with sink points for update summarization (PD, JG, JZ, XC), pp. 1757–1760.
CIKM-2010-GuoCXS #approach #query #recommendation #social: A structured approach to query recommendation with social annotation data (JG, XC, GX, HS), pp. 619–628.
SIGIR-2009-GuoXCL #query #recognition: Named entity recognition in query (JG, GX, XC, HL), pp. 267–274.
SIGIR-2008-GuoXLC #query #refinement: A unified and discriminative model for query refinement (JG, GX, HL, XC), pp. 379–386.