BibSLEIGH corpus
BibSLEIGH tags
BibSLEIGH bundles
BibSLEIGH people
Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
email twitter
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 DBLP: Konstan:Joseph_A=

Facilitated 2 volumes:

CHI 2012Ed
RecSys 2007Ed

Contributed to:

RecSys 20152015
RecSys 20142014
RecSys 20132013
CSCW 20122012
CHI 20112011
RecSys 20112011
CIKM 20102010
RecSys 20102010
CHI 20092009
CHI 20082008
RecSys 20082008
SIGMOD 20082008
CHI 20072007
ECDL 20072007
RecSys 20072007
CSCW 20062006
CHI 20032003
CIKM 20022002
CSCW 20022002
CSCW 20002000
SIGIR 19991999
CSCW 19981998
OOPSLA 19911991
JCDL 20042004

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.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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