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: Shi:Yue
Contributed to:
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.