BibSLEIGH — Joseph_A

Travelled to:

1 × Austria
1 × China
1 × Italy
1 × Spain
1 × Switzerland
13 × USA
4 × Canada

Collaborated with:

J.Riedl F.M.Harper A.Pal M.D.Ekstrand S.M.McNee D.Kluver J.L.Herlocker N.Kapoor ∅ B.P.Bailey L.A.Rowe J.A.Stemper J.T.Butler A.Borchers J.Margatan X.Dong D.Moy J.B.Schafer D.Cosley S.K.Lam I.Albert G.C.Fouty M.C.Willemsen F.Mourão L.C.d.Rocha W.M.Jr. R.Farzan R.Kraut M.Ludwig P.Resnick A.Hotho J.Pindado D.R.Raban S.Rafaeli N.Good J.Grossklags D.K.Mulligan K.Kapoor V.Kumar L.G.Terveen P.R.Schrater V.Krishnan P.K.Narayanashetty M.Nathan R.T.Davies R.Torres M.Abel P.Kannan J.D.Weisz S.B.Kiesler H.Zhang Y.Ren R.E.Kraut B.M.Sarwar B.N.Miller J.Chen P.Gopalkrishnan A.M.Rashid

Talks about:

recommend (13) user (8) research (6) system (5) onlin (5) question (4) filter (4) algorithm (3) collabor (3) prefer (3)

Person: Joseph A. Konstan

DBLP: Konstan:Joseph_A=

Facilitated 2 volumes:

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2003

2002

2000

1999

1998

1991

2004

Wrote 30 papers:

RecSys-2015-EkstrandKHK #algorithm #case study #recommendation: Letting Users Choose Recommender Algorithms: An Experimental Study (MDE, DK, FMH, JAK), pp. 11–18.
RecSys-2015-KapoorKTKS #adaptation #quote: “I like to explore sometimes”: Adapting to Dynamic User Novelty Preferences (KK, VK, LGT, JAK, PRS), pp. 19–26.
RecSys-2014-EkstrandHWK #algorithm #difference #recommendation: User perception of differences in recommender algorithms (MDE, FMH, MCW, JAK), pp. 161–168.
RecSys-2014-KluverK #behaviour #recommendation: Evaluating recommender behavior for new users (DK, JAK), pp. 121–128.
RecSys-2013-MouraoRKM #hybrid #recommendation: Exploiting non-content preference attributes through hybrid recommendation method (FM, LCdR, JAK, WMJ), pp. 177–184.
CSCW-2012-FarzanKPK #community #empirical #online #volunteer: Socializing volunteers in an online community: a field experiment (RF, RK, AP, JAK), pp. 325–334.
CSCW-2012-PalMK #identification: Question temporality: identification and uses (AP, JM, JAK), pp. 257–260.
CHI-2011-DongHK #interface #performance: Entity-linking interfaces in user-contributed content: preference and performance (XD, FMH, JAK), pp. 2187–2196.
RecSys-2011-EkstrandLKR #ecosystem #recommendation #research: Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit (MDE, ML, JAK, JR), pp. 133–140.
CIKM-2010-PalK #bias #community #identification: Expert identification in community question answering: exploring question selection bias (AP, JAK), pp. 1505–1508.
RecSys-2010-EkstrandKSBKR #automation #research: Automatically building research reading lists (MDE, PK, JAS, JTB, JAK, JR), pp. 159–166.
RecSys-2010-ResnickKHP #contest #named #question: Contests: way forward or detour? (PR, JAK, AH, JP), pp. 37–38.
CHI-2009-HarperMK #social: Facts or friends?: distinguishing informational and conversational questions in social Q&A sites (FMH, DM, JAK), pp. 759–768.
CHI-2008-HarperRRK #online #predict #quality: Predictors of answer quality in online Q&A sites (FMH, DRR, SR, JAK), pp. 865–874.
RecSys-2008-KrishnanNNDK #online #predict #recommendation: Who predicts better?: results from an online study comparing humans and an online recommender system (VK, PKN, MN, RTD, JAK), pp. 211–218.
SIGMOD-2008-Konstan #recommendation: Introduction to recommender systems (JAK), pp. 1373–1374.
CHI-2007-GoodGMK #empirical #scalability: Noticing notice: a large-scale experiment on the timing of software license agreements (NG, JG, DKM, JAK), pp. 607–616.
CHI-2007-WeiszKZRKK #chat #video: Watching together: integrating text chat with video (JDW, SBK, HZ, YR, REK, JAK), pp. 877–886.
ECDL-2007-KapoorBMFSK #case study #online: A Study of Citations in Users’ Online Personal Collections (NK, JTB, SMM, GCF, JAS, JAK), pp. 404–415.
RecSys-2007-KapoorCBFSRK #named #research: Techlens: a researcher’s desktop (NK, JC, JTB, GCF, JAS, JR, JAK), pp. 183–184.
CSCW-2006-McNeeKK #recommendation #research: Don’t look stupid: avoiding pitfalls when recommending research papers (SMM, NK, JAK), pp. 171–180.
CHI-2003-BaileyK #design #multi #tool support: Are informal tools better?: comparing DEMAIS, pencil and paper, and authorware for early multimedia design (BPB, JAK), pp. 313–320.
CHI-2003-CosleyLAKR #how #interface #recommendation: Is seeing believing?: how recommender system interfaces affect users’ opinions (DC, SKL, IA, JAK, JR), pp. 585–592.
CIKM-2002-SchaferKR #integration #recommendation: Meta-recommendation systems: user-controlled integration of diverse recommendations (JBS, JAK, JR), pp. 43–51.
CSCW-2002-McNeeACGLRKR #on the #recommendation #research: On the recommending of citations for research papers (SMM, IA, DC, PG, SKL, AMR, JAK, JR), pp. 116–125.
CSCW-2000-HerlockerKR #collaboration #recommendation: Explaining collaborative filtering recommendations (JLH, JAK, JR), pp. 241–250.
SIGIR-1999-HerlockerKBR #algorithm #collaboration #framework: An Algorithmic Framework for Performing Collaborative Filtering (JLH, JAK, AB, JR), pp. 230–237.
CSCW-1998-SarwarKBHMR #collaboration #predict #quality #research #using: Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System (BMS, JAK, AB, JLH, BNM, JR), pp. 345–354.
OOPSLA-1991-KonstanR #object-oriented #programming #using: Developing a GUIDE Using Object-Oriented Programming (JAK, LAR), pp. 75–88.
JCDL-2004-TorresMAKR #library: Enhancing digital libraries with TechLens+ (RT, SMM, MA, JAK, JR), pp. 228–236.