Travelled to:1 × Austria
1 × China
1 × Ireland
1 × Spain
2 × USA
Collaborated with:J.Riedl J.A.Konstan M.Ludwig D.Kluver F.M.Harper M.C.Willemsen T.T.Nguyen J.Kolb S.Sen J.J.Levandoski A.Eldawy M.F.Mokbel P.Kannan J.A.Stemper J.T.Butler T.Wang P.Hui
Talks about:recommend (8) algorithm (3) user (3) research (2) rate (2) len (2) kit (2) architectur (1) experiment (1) framework (1)
Person: Michael D. Ekstrand
DBLP: Ekstrand:Michael_D=
Contributed to:
20152014201320122011
20112010Wrote 9 papers:
- RecSys-2015-EkstrandKHK #algorithm #case study #recommendation
- Letting Users Choose Recommender Algorithms: An Experimental Study (MDE, DK, FMH, JAK), pp. 11–18.
- RecSys-2014-EkstrandHWK #algorithm #difference #recommendation
- User perception of differences in recommender algorithms (MDE, FMH, MCW, JAK), pp. 161–168.
- RecSys-2013-NguyenKWHEWR #experience #rating #recommendation #user interface
- Rating support interfaces to improve user experience and recommender accuracy (TTN, DK, TYW, PMH, MDE, MCW, JR), pp. 149–156.
- RecSys-2012-EkstrandR #algorithm #predict #recommendation
- When recommenders fail: predicting recommender failure for algorithm selection and combination (MDE, JR), pp. 233–236.
- RecSys-2012-KluverNESR #how #question #rating
- How many bits per rating? (DK, TTN, MDE, SS, JR), pp. 99–106.
- RecSys-2011-EkstrandLKR #ecosystem #recommendation #research
- Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit (MDE, ML, JAK, JR), pp. 133–140.
- RecSys-2011-EkstrandLKR11a #composition #framework #named #recommendation
- LensKit: a modular recommender framework (MDE, ML, JK, JR), pp. 349–350.
- VLDB-2011-LevandoskiELEMR #architecture #benchmark #metric #named #performance #recommendation
- RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures (JJL, MDE, ML, AE, MFM, JR), pp. 911–920.
- RecSys-2010-EkstrandKSBKR #automation #research
- Automatically building research reading lists (MDE, PK, JAS, JTB, JAK, JR), pp. 159–166.