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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; Li Zhangy; Zhong Suy; Juanzi Li In: http://keg.cs.tsinghua.edu.cn/jietang/publications/SIGIR11-Yang-et-al-social-context-summarization.pdf Abstract: We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on ex-tracting informative sentences from standard documents. With the rapid growth of online social networks, abundant user generated content (e.g., comments) associated with the standard documents is available. Which parts in a document are social users really caring about? How can we generate summaries for standard documents by considering both the informativeness of sentences and interests of social users? This paper explores such an approach by model-ing Web documents and social contexts into a unified framework. We propose a dual wing factor graph (DWFG) model, which uti-lizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries. An efficient al-gorithm is designed to learn the proposed factor graph model. Ex-perimental results on a Twitter data set validate the effectiveness of the proposed model. By leveraging the social context information, our approach obtains significant improvement (averagely +5.0%-17.3%) over several alternative methods (CRF, SVM, LR, PR, and DocLead) on the performance of summarization. Keyword: Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval; Content Analysis and Indexing; Data Mining; Experimentation Keywords Document summarization; Factor graph; H.2.8 [Database Management; J.4 [Computer Applications; Social and Behavioral Sciences General Terms Algorithms; Social context; Twitter URL: http://keg.cs.tsinghua.edu.cn/jietang/publications/SIGIR11-Yang-et-al-social-context-summarization.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.644.9492 BASE Hide 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 BASE Show 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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