PDF-LinearProgrammingBoostingforUnevenDatasetsJurijLeskovecJurij.Leskovec@

GivenandatrainingsetSx1y1xmymwherexi2Xyi2f11gInitialiseD1iwifyi1welsewherewwandPm1D1i1Fort1TPassdistributionDttoweaklearnerGetweakhypothesishtXRChooset2RUp. cornelledu larscscornelledu kleinbercscornelledu ABSTRACT Tracking new topics ideas and memes across the Web has been an issue of considerable interest Recent work has developed meth ods for tracking topic shifts over long time scales as well as abru 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 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 stanfordedu mmmedinastanfordedu Stanford University ABSTRACT Transmission of infectious diseases propagation of infor mation and spread of ideas and in64258uence through social networks are all examples of diffusion In such cases we say that a contag stanfordedu Jure Leskovec Stanford USA jurecsstanfordedu Abstract Our personal social networks are big and cluttered and currently there is no good way to organize them Social networking sites allow users to manually categorize their friends into soc 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. Overlapping Communities. 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:. 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 . Introduction to Recommender Systems. Recommender systems: The task. Customer W. 2. Slides adapted from Jure Leskovec. Plays an Ella Fitzgerald song. What should we recommend next?. Thomas . Quella. Wikimedia Commons.