Travelled to:1 × Australia
1 × Ireland
1 × Spain
1 × Switzerland
2 × China
5 × USA
Collaborated with:L.Baltrunas Y.Shi M.Larson A.Hanjalic M.Weimer N.Oliver J.S.Pedro ∅ T.V.Nguyen M.Bruch A.J.Smola S.Vargas P.Castells K.Church M.Böhmer X.Amatriain
Talks about:recommend (9) awar (7) context (6) collabor (5) rank (4) system (3) filter (3) factor (3) learn (3) reciproc (2)
Person: Alexandros Karatzoglou
DBLP: Karatzoglou:Alexandros
Contributed to:
2014
2014
201420132012201220122011201020092008Wrote 13 papers:
- CIKM-2014-ShiKBLH #learning #named #recommendation
- CARS2: Learning Context-aware Representations for Context-aware Recommendations (YS, AK, LB, ML, AH), pp. 291–300.
- RecSys-2014-PedroK #collaboration #recommendation
- Question recommendation for collaborative question answering systems with RankSLDA (JSP, AK), pp. 193–200.
- RecSys-2014-VargasBKC #recommendation
- Coverage, redundancy and size-awareness in genre diversity for recommender systems (SV, LB, AK, PC), pp. 209–216.
- SIGIR-2014-NguyenKB #process #recommendation
- Gaussian process factorization machines for context-aware recommendations (TVN, AK, LB), pp. 63–72.
- 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.
- CIKM-2012-KaratzoglouBCB #mobile #recommendation
- Climbing the app wall: enabling mobile app discovery through context-aware recommendations (AK, LB, KC, MB), pp. 2527–2530.
- 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.
- RecSys-2011-Karatzoglou #collaboration #modelling #order
- Collaborative temporal order modeling (AK), pp. 313–316.
- RecSys-2010-KaratzoglouABO #collaboration #multi #recommendation
- Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering (AK, XA, LB, NO), pp. 79–86.
- RecSys-2009-WeimerKB #matrix #recommendation
- Maximum margin matrix factorization for code recommendation (MW, AK, MB), pp. 309–312.
- RecSys-2008-WeimerKS #adaptation #collaboration
- Adaptive collaborative filtering (MW, AK, AJS), pp. 275–282.