BibSLEIGH — Yanyan

Travelled to:

1 × Australia
1 × Canada
1 × Chile
1 × China
1 × Finland
1 × Ireland
1 × Italy
1 × Russia
1 × The Netherlands
3 × USA

Collaborated with:

J.Guo X.Cheng S.Niu P.Wang X.Zhu T.Liu Z.Ma H.Li C.Fan J.Xu S.Wan L.Bai L.Xia X.Geng Y.Zhu W.Nejdl Z.Lin T.Qin X.Yuan C.Wu Z.Wang J.Li P.Yew J.Huang X.Feng Y.Chen Y.Guan

Talks about:

learn (7) rank (7) recommend (6) model (5) queri (3) data (3) top (3) general (2) analysi (2) search (2)

Person: Yanyan Lan

DBLP: Lan:Yanyan

Contributed to:

2015

2014

2013

2012

2009

2008

Wrote 16 papers:

ICSE-v1-2015-YuanWWLYHFLCG #concurrent #debugging #named #using: ReCBuLC: Reproducing Concurrency Bugs Using Local Clocks (XY, CW, ZW, JL, PCY, JH, XF, YL, YC, YG), pp. 824–834.
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-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.
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.