Travelled to:1 × Austria
1 × Ireland
1 × Italy
1 × Spain
1 × The Netherlands
4 × USA
Collaborated with:A.Bellogín B.Loni B.J.Jain S.Albayrak D.Tikk ∅ A.Hotho T.Samar A.P.d.Vries M.Larson A.Hanjalic S.Dooms B.Fields S.Berkovsky E.W.D.Luca J.Hermanns S.Narr T.Plumbaum C.Scheel
Talks about:recommend (11) system (8) challeng (5) evalu (5) collabor (3) filter (3) user (3) contextu (2) start (2) cold (2)
Person: Alan Said
DBLP: Said:Alan
Contributed to:
2015
20142014
20132012
2012
201220112010Wrote 13 papers:
- RecSys-2015-SaidB #evaluation #recommendation
- Replicable Evaluation of Recommender Systems (AS, AB), pp. 363–364.
- ECIR-2014-BelloginSVS #challenge #dataset #evaluation #web
- Challenges on Combining Open Web and Dataset Evaluation Results: The Case of the Contextual Suggestion Track (AB, TS, APdV, AS), pp. 430–436.
- RecSys-2014-LoniS #library #named #recommendation
- WrapRec: an easy extension of recommender system libraries (BL, AS), pp. 377–378.
- RecSys-2014-LoniSLH #collaboration
- “Free lunch” enhancement for collaborative filtering with factorization machines (BL, AS, ML, AH), pp. 281–284.
- RecSys-2014-SaidB #benchmark #comparative #evaluation #framework #metric #recommendation
- Comparative recommender system evaluation: benchmarking recommendation frameworks (AS, AB), pp. 129–136.
- RecSys-2014-SaidB14a #evaluation #named #recommendation #tool support
- Rival: a toolkit to foster reproducibility in recommender system evaluation (AS, AB), pp. 371–372.
- RecSys-2014-SaidDLT #challenge #recommendation
- Recommender systems challenge 2014 (AS, SD, BL, DT), pp. 387–388.
- CSCW-2013-SaidFJA #algorithm #collaboration #evaluation #recommendation
- User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm (AS, BF, BJJ, SA), pp. 1399–1408.
- RecSys-2012-SaidTH #challenge #recommendation
- The challenge of recommender systems challenges (AS, DT, AH), pp. 9–10.
- SAC-2012-SaidJA #collaboration
- Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users (AS, BJJ, SA), pp. 2035–2040.
- SIGIR-2012-SaidJNPAS #case study #recommendation #user study
- Estimating the magic barrier of recommender systems: a user study (AS, BJJ, SN, TP, SA, CS), pp. 1061–1062.
- RecSys-2011-SaidBLH #challenge #recommendation
- Challenge on context-aware movie recommendation: CAMRa2011 (AS, SB, EWDL, JH), pp. 385–386.
- RecSys-2010-Said #hybrid #identification #recommendation
- Identifying and utilizing contextual data in hybrid recommender systems (AS), pp. 365–368.