Travelled to:1 × Australia
1 × Ireland
1 × Spain
1 × Switzerland
2 × China
6 × USA
Collaborated with:A.Karatzoglou F.Ricci Y.Shi M.Larson A.Hanjalic N.Oliver B.Ludwig ∅ T.V.Nguyen O.Moling T.Makcinskas S.Vargas P.Castells K.Church M.Böhmer S.Peer X.Amatriain
Talks about:recommend (12) context (9) awar (9) collabor (4) filter (4) rank (4) system (3) factor (3) optim (3) learn (3)
Person: Linas Baltrunas
DBLP: Baltrunas:Linas
Contributed to:
2014
2014
20142013201220122012
20112011201020092008Wrote 15 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-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-MolingBR #feedback #recommendation
- Optimal radio channel recommendations with explicit and implicit feedback (OM, LB, FR), pp. 75–82.
- 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.
- DUXU-v1-2011-BaltrunasLPR #mobile #recommendation
- Context-Aware Places of Interest Recommendations for Mobile Users (LB, BL, SP, FR), pp. 531–540.
- RecSys-2011-BaltrunasLR #matrix #recommendation
- Matrix factorization techniques for context aware recommendation (LB, BL, FR), pp. 301–304.
- RecSys-2010-BaltrunasMR #collaboration #rank #recommendation
- Group recommendations with rank aggregation and collaborative filtering (LB, TM, FR), pp. 119–126.
- 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-BaltrunasR #collaboration
- Context-based splitting of item ratings in collaborative filtering (LB, FR), pp. 245–248.
- RecSys-2008-Baltrunas #information management #recommendation
- Exploiting contextual information in recommender systems (LB), pp. 295–298.