PDF-LinearProgrammingBoostingforUnevenDatasetsJurijLeskovecJurij.Leskovec@

GivenandatrainingsetSx1y1xmymwherexi2Xyi2f11gInitialiseD1iwifyi1welsewherewwandPm1D1i1Fort1TPassdistributionDttoweaklearnerGetweakhypothesishtXRChooset2RUp. stanfordedu dph kleinbercscornelledu jurecsstanfordedu ABSTRACT An increasingly common feature of online communities and social media sites is a mechanism for rewarding user achievements based on a system of badges Badges are given to users for part stanfordedu sudhof jurafsky cgpottsstanfordedu Abstract We propose a computational framework for identifying linguistic aspects of polite ness Our starting point is a new corpus of requests annotated for politeness which we use to evaluate aspects of stanfordedu cristianmpiswsorg Abstract Social media systems rely on user feedback and rating mechanisms for personalization ranking and content 64257ltering However when users evaluate content con tributed by fellow users eg by liking a post or votin cmuedu Jure Leskovec jurecscmuedu Carlos Guestrin guestrincscmuedu School of Computer Science Carnegie Mellon University Pittsburgh PA USA Abstract We present a uni64257ed model of what was tradi tionally viewed as two separate tasks data asso ciatio edu Jure Leskovec CS Department Stanford University jurecsstanfordedu Abstract Social media forms a central domain for the production and dissemination of realtime information Even though such 64258ows of information have traditionally been thought o edu Jure Leskovec Stanford University jurecsstanfordedu ABSTRACT Online content exhibits rich temporal dynamics and divers e real time user generated content further intensi64257es this proces s How ever temporal patterns by which online content grow edu Jure Leskovec Stanford University jurecsstanfordedu Abstract Nodes in realworld networks organize into densely linked communities where edges appear with high con centration among the members of the community Identifying such communities of nodes Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. SVD & CUR. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. Latent Factor Models. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. Course Introduction. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. Ranking Nodes on the Graph. Web pages are not equally “important”. www.joe-schmoe.com. vs. . www.stanford.edu. . Since there is large diversity . in the connectivity of the . web graph we can .