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Travelled to:
1 × Australia
1 × Spain
1 × The Netherlands
2 × China
2 × Ireland
2 × USA
Collaborated with:
M.Larson A.Hanjalic A.Karatzoglou L.Baltrunas N.Oliver E.Zhong N.Liu S.Rajan B.Loni X.Zhao J.Wang
Talks about:
recommend (6) collabor (5) filter (5) learn (4) rank (4) awar (4) context (3) factor (3) user (3) reciproc (2)

Person: Yue Shi

DBLP DBLP: Shi:Yue

Contributed to:

KDD 20152015
CIKM 20142014
ECIR 20142014
RecSys 20132013
RecSys 20122012
SIGIR 20122012
ECIR 20112011
RecSys 20102010
RecSys 20092009

Wrote 12 papers:

KDD-2015-ZhongLSR #recommendation #scalability
Building Discriminative User Profiles for Large-scale Content Recommendation (EZ, NL, YS, SR), pp. 2277–2286.
CIKM-2014-ShiKBLH #learning #named #recommendation
CARS2: Learning Context-aware Representations for Context-aware Recommendations (YS, AK, LB, ML, AH), pp. 291–300.
ECIR-2014-LoniSLH #collaboration
Cross-Domain Collaborative Filtering with Factorization Machines (BL, YS, ML, AH), pp. 656–661.
RecSys-2013-KaratzoglouBS #learning #rank #recommendation
Learning to rank for recommender systems (AK, LB, YS), pp. 493–494.
RecSys-2013-ShiKBLH #multi #named #optimisation #rank
xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance (YS, AK, LB, ML, AH), pp. 431–434.
RecSys-2012-ShiKBLOH #collaboration #learning #named #rank
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (YS, AK, LB, ML, NO, AH), pp. 139–146.
SIGIR-2012-ShiKBLHO #named #optimisation #recommendation
TFMAP: optimizing MAP for top-n context-aware recommendation (YS, AK, LB, ML, AH, NO), pp. 155–164.
SIGIR-2012-ShiZWLH #adaptation #recommendation
Adaptive diversification of recommendation results via latent factor portfolio (YS, XZ, JW, ML, AH), pp. 175–184.
ECIR-2011-ShiLH #collaboration #multi #ranking #self
Reranking Collaborative Filtering with Multiple Self-contained Modalities (YS, ML, AH), pp. 699–703.
ECIR-2011-ShiLH11a #how #question #recommendation #trust
How Far Are We in Trust-Aware Recommendation? (YS, ML, AH), pp. 704–707.
RecSys-2010-ShiLH #collaboration #learning #matrix #rank
List-wise learning to rank with matrix factorization for collaborative filtering (YS, ML, AH), pp. 269–272.
RecSys-2009-ShiLH #collaboration #similarity
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering (YS, ML, AH), pp. 125–132.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.