BibSLEIGH — Yi

Travelled to:

1 × Australia
1 × Canada
1 × Chile
1 × Switzerland
1 × United Kingdom
3 × China
5 × USA

Collaborated with:

Z.Zheng A.Dong J.Bian H.Zha B.Long H.Deng ∅ C.Liao S.Yang L.Li C.Kang J.Lee W.Kong A.Zhang B.L.Tseng J.Tang X.Hu H.Liu Y.Inagaki M.Maki A.Kotov P.Kolari L.Duan Z.Kou M.Wu J.Zhang X.Kong R.J.Luo P.S.Yu A.J.Smola X.He S.Reddy S.Vadrevu R.Li J.Luo J.Allan R.A.Baeza-Yates Y.Li H.Wang C.Zhai L.Jie S.Lamkhede R.Sapra E.Hsu H.Song T.Moon L.Li W.Chu O.Chapelle Y.Zhang X.Li F.Li S.Ji A.Goyal C.A.Gunter J.Han J.Bai K.Zhou G.Xue G.Sun

Talks about:

search (14) rank (11) queri (6) learn (6) predict (5) web (5) user (4) data (4) base (4) feedback (3)

Person: Yi Chang

DBLP: Chang:Yi

Contributed to:

2015

2014

2013

2012

2011

2010

2009

Wrote 24 papers:

SIGIR-2015-Chang #web: From Web Search Relevance to Vertical Search Relevance (YC), p. 1073.
SIGIR-2015-KongLLZCA #predict: Predicting Search Intent Based on Pre-Search Context (WK, RL, JL, AZ, YC, JA), pp. 503–512.
SIGIR-2015-LiDDCZB #behaviour #markov #process #query: Analyzing User’s Sequential Behavior in Query Auto-Completion via Markov Processes (LL, HD, AD, YC, HZ, RABY), pp. 123–132.
SIGIR-2015-ZhangGKDDCGH #adaptation #feedback #named #query: adaQAC: Adaptive Query Auto-Completion via Implicit Negative Feedback (AZ, AG, WK, HD, AD, YC, CAG, JH), pp. 143–152.
CIKM-2014-TangHCL #interactive #predict: Predictability of Distrust with Interaction Data (JT, XH, YC, HL), pp. 181–190.
CIKM-2014-ZhangKLCY #approach #named #scalability: NCR: A Scalable Network-Based Approach to Co-Ranking in Question-and-Answer Sites (JZ, XK, RJL, YC, PSY), pp. 709–718.
KDD-2014-LiDDCZ #identification #process: Identifying and labeling search tasks via query-based hawkes processes (LL, HD, AD, YC, HZ), pp. 731–740.
SIGIR-2014-LiDWDCZ #2d #query: A two-dimensional click model for query auto-completion (YL, AD, HW, HD, YC, CZ), pp. 455–464.
KDD-2013-JieLSHSC #feedback #online: A unified search federation system based on online user feedback (LJ, SL, RS, EH, HS, YC), pp. 1195–1203.
CIKM-2012-KangLC #category theory #predict: Predicting primary categories of business listings for local search (CK, JL, YC), pp. 2591–2594.
CIKM-2012-LongBDC #e-commerce #predict: Enhancing product search by best-selling prediction in e-commerce (BL, JB, AD, YC), pp. 2479–2482.
SIGIR-2012-YangSLZC #network #predict #social: Friend or frenemy?: predicting signed ties in social networks (SHY, AJS, BL, HZ, YC), pp. 555–564.
CIKM-2011-BianC #classification #query #taxonomy: A taxonomy of local search: semi-supervised query classification driven by information needs (JB, YC), pp. 2425–2428.
CIKM-2011-DongBHRC #optimisation #personalisation #recommendation: User action interpretation for personalized content optimization in recommender systems (AD, JB, XH, SR, YC), pp. 2129–2132.
SIGIR-2011-InagakiBCM #mobile #using #web: Enhancing mobile search using web search log data (YI, JB, YC, MM), pp. 1201–1202.
CIKM-2010-KotovKDC #profiling #query #ranking #web: Temporal query log profiling to improve web search ranking (AK, PK, LD, YC), pp. 1149–1158.
CIKM-2010-KouCZZ #learning #ranking: Learning to blend rankings: a monotonic transformation to blend rankings from heterogeneous domains (ZK, YC, ZZ, HZ), pp. 1921–1924.
CIKM-2010-LongCVYZ #ranking: Ranking with auxiliary data (BL, YC, SV, SHY, ZZ), pp. 1489–1492.
CIKM-2010-MoonLCLZC #feedback #learning #online #ranking #realtime #using: Online learning for recency search ranking using real-time user feedback (TM, LL, WC, CL, ZZ, YC), pp. 1501–1504.
SIGIR-2010-LongCZCZT #learning #optimisation #ranking: Active learning for ranking through expected loss optimization (BL, OC, YZ, YC, ZZ, BLT), pp. 267–274.
CIKM-2009-BaiZXZSTZC #learning #multi #rank #web: Multi-task learning for learning to rank in web search (JB, KZ, GRX, HZ, GS, BLT, ZZ, YC), pp. 1549–1552.
CIKM-2009-LiLJZCD #evaluation #ranking #robust #web: Incorporating robustness into web ranking evaluation (XL, FL, SJ, ZZ, YC, AD), pp. 2007–2010.
CIKM-2009-WuCZZ #approach #definite clause grammar #learning #novel #rank #using: Smoothing DCG for learning to rank: a novel approach using smoothed hinge functions (MW, YC, ZZ, HZ), pp. 1923–1926.
SIGIR-2009-ChangDLZ #ranking #topic: Enhancing topical ranking with preferences from click-through data (YC, AD, CL, ZZ), pp. 666–667.