Ed Chi

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Ed H. Chi is a Principal Scientist at Google, leading a machine learning research team focused on recommendation systems and social computing research. He has launched significant improvements of recommenders for YouTube, Google Play Store and Google+. With 39 patents and over 110 research articles, he is known for research on Web and online social systems, and the effects of social signals on user behavior. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on software systems since 1993. Recognized as an ACM Distinguished Scientist, he has been featured and quoted in the press.

research wikipedia hci asc twitter social keynote cognition parc mturk social media search web2.0 crowd dynamics editorship wisdom social computing 2011 amazon edchi recommender conference web20 wikisym design chi2009 mrtaggy evaluation lab augmented web tagging wikidashboard focused learning deep retrieval machine learning ml platform 2012 korea analytics behavior language location invited talk sss human computer interaction aaai mechanical turk social stream languages usage social annotation elearning education2.0 cscl duplication social sciences replication tutorial user study clustering browser browsing client topic social analytics data analytics large scale delicious model-based chi2010 passages highlighting index iui ebook hcir hypertext msm2010 msm10 modeling talk acm foraging user modeling infoscent infoforaging overview workshop social tagging tagsearch evolution growth model population analysis editor activity social community wisdomofcrowd shifts dynamic changes 2009 models laboratories ecological methodology method paper living position forum feedback relevance tag-based chi2007 conflict

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Aktuellste Beliebteste SlideShares

2017 10-10 (netflix ml platform meetup) learning item and user representations with sparse data in recommender systems (focused learning, factorized deep retrieval)

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Ed Chi

2017 10-10 (netflix ml platform meetup) learning item and user representations with sparse data in recommender systems (focused learning, factorized deep retrieval)

HCI Korea 2012 Keynote Talk on Model-Driven Research in Social Computing

Location and Language in Social Media (Stanford Mobi Social Invited Talk)

Crowdsourcing using MTurk for HCI research

CIKM 2011 Social Computing Industry Invited Talk

WikiSym 2011 Closing Keynote

CSCL 2011 Keynote on Social Computing and eLearning

Replication is more than Duplication: Position slides for CHI2011 panel on replication of HCI research

Tutorial on Using Amazon Mechanical Turk (MTurk) for HCI Research

Crowdsourcing for HCI Research with Amazon Mechanical Turk

Eddi: Topic Browsing of Twitter Streams

Large Scale Social Analytics on Wikipedia, Delicious, and Twitter (presented at IBM NPUC 2010)

Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010

Zerozero88 Twitter URL Item Recommender

Smart eBooks: ScentIndex and ScentHighlight research published at VAST2006

Model-Driven Research in Social Computing

ASC Disaster Response Proposal from Aug 2007

Using Information Scent to Model Users in Web1.0 and Web2.0

China HCI Symposium 2010 March: Augmented Social Cognition Research from PARC, by Ed H. Chi

2010-03-10 PARC Augmented Social Cognition Research Overview