Apple Inc. / Amazon / Netflix / Ericsson / EMC / Google / Facebook / Cisco / Pearson / Correction Model / NetApp / Xc0 / HP / VMware / Yahoo! / Intel / Microsoft / Significance Test / /
recommendation algorithms / information theoretic polynomial function search / model search / online choices / prototype crossplatform web/mobile application / explanation systems / constant time collaborative filtering algorithm / online forum / online product choice / recommender systems / finance / online content / recommendation algorithm / change / Web Science Conference / online communities / online retailers / social networks / online comments / online discussion forum / Web Services / /
Organization
Human Subjects committee / National Science Foundation / International Monetary Fund / International World Wide Web Conferences Steering Committee / UC Berkeley / National Bureau of Economic Research / Blum Center for Developing Economies and the Development Impact Lab / National Academy of Sciences / BIC Likelihood College / Stacey Siebel Foundation / American Psychological Association / United States Agency for International Development / Oxford University / Ken Goldberg Department of Electrical Engineering and Computer Sciences / /
Person
Brandie Nonnecke / Wc / Camille Crittenden / Solomon Asch / Daniel Catterson / Gavin Newsom / Ken Goldberg / Marijuana Marriage / H. Hong / Allen Huang / Tanja Aitamurto / John Scott / Henry Brady / Michael J. Franklin / Jay Patel / /
Position
Statistician / model for each category / Lt. Governor / author / model / representative / /
Product
Non-parametric Significance Test / /
ProvinceOrState
California / /
PublishedMedium
Proceedings of the National Academy of Sciences / The American Statistician / The Quarterly Journal of Economics / /
A Methodology for Learning, Analyzing, and Mitigating Social Influence Bias in Recommender Systems Sanjay Krishnan, Jay Patel, Michael J. Franklin, Ken Goldberg Department of Electrical Engineering and Computer Sciences,