PDF-How Community Feedback Shapes User Behavior Justin Cheng Cristian DanescuNiculescuMizil

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. 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 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 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 Biomedical Informatics Stanford University plependu nigamstanfordedu ABSTRACT Event sequences such as patients medical histories or users se quences of product reviews trace how individuals progress over time Identifying common patterns o Guns are cool. In The Beginning: The Barriers. Nervous about age gap. Unsure of common interests. Time constraints. Determining what to do for our Dyad experience. Do we trust and respect each other?. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. CS224W: Social and Information Network Analysis. Jure Leskovec, . Stanford University. http://cs224w.stanford.edu. Overlapping Communities. Non-overlapping vs. overlapping communities. 11/10/2010. Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu. Information Flow in Networks. CS224W: Social and Information Network Analysis. Jure Leskovec, . Stanford University. http://cs224w.stanford.edu. Tracking the Information Flow. Imagine you want to track the flow of information. CS224W: Social and Information Network Analysis. Jure . Leskovec. , . Stanford University. http://cs224w.stanford.edu. Today: Signed Networks. Networks with . positive. . and . negative. relationships. Decision Trees on MapReduce CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu Decision Tree Learning Give one attribute (e.g., lifespan), try to predict the value of new people’s lifespans by means of some of the other available attribute Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Learning through Experimentation. Web advertising. We discussed how to . match advertisers to . queries in real-time . [ENG] - - Sodi W Series 2020 - v1 Last Update - 30/12/2019 1 Official Regulation SODI W SERIES - 2020 Season - [ENG] - SWS Official Regulation - Sodi W Series 2020 - v1 Last Update - 30/12/2019 2 1 semester2 semester3semester4semestercreditsV//MV//MV//MV//MBIWO-01Building Materials4/2/0PL8BIWO-02ContinuumMechanics TensorAnalysis4/2/0 2PL8BIWO-03Energy Methods FEM4/2/0 2 PL8BIWO-04Numerical Met