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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1...270 271 272 273 274 Hits 5.461 – 5.465 of 5.465 5461 Mark my words! Linguistic Cristian Danescu-niculescu-mizil; Michael Gamon; Susan Dumais In: http://research.microsoft.com/en-us/um/people/sdumais/fr862-danescu-niculescu-mizil.pdf BASE Show details 5462 CoupledLP: Link Prediction in Coupled Networks In: http://keg.cs.tsinghua.edu.cn/jietang/publications/kdd15-Dong-et-al-link-prediction-in-coupled-networks.pdf BASE Show details 5463 2011b. Social Context Summarization Zi Yang; Keke Caiy; Jie Tang... In: http://keg.cs.tsinghua.edu.cn/jietang/publications/SIGIR11-Yang-et-al-social-context-summarization.pdf BASE Show details 5464 The Effects of Feedback on Human Behavior in Social Media: An Inverse Reinforcement Learning Model Sanmay Das; Allen Lavoie In: http://www.cse.wustl.edu/%7Eallenlavoie/papers/reddit.pdf Abstract: We introduce and validate a learning model of human behavior change in response to feedback on social media. People who partic-ipate in these types of websites, like Wikipedia, Reddit, and others, are learning agents whose choices about how to allocate their ef-fort are dynamic and responsive to how they feel their efforts were received in the past. By explicitly taking into account the reinforce-ment effects of different types of feedback received on prior con-tributions, our model is able to significantly outperform all known baselines in predicting future contributions both on synthetic data and on real data collected from the social news site reddit.com. Our model has an intuitive interpretation as users playing mixed strate-gies in a game-like setting with thousands of other users and thou-sands of available pure strategies. In this interpretation, our task is then inverse reinforcement learning: recovering users ’ reward functions based on observed behavior. Keyword: Categories and Subject Descriptors J.4 [Social and Behavioral Sciences; Distributed Artificial Intelligence Keywords Social Media; Economics; I.2.11 [Artificial Intelligence; Multi-Agent Learning; Social Simulation URL: http://www.cse.wustl.edu/%7Eallenlavoie/papers/reddit.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.680.3242 BASE Hide details 5465 ABSTRACT Alexander Kress In: http://www.cs.toronto.edu/~cebly/Papers/limPrecBid-draft.pdf BASE Show details Page: 1...270 271 272 273 274

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