Deep Crossing: Web-Scale Modeling without Manually Crafted ...
advertiser, and the search platform. The goal of the platform is to show the user the advertisement that best matches the user’s intent, which was expressed mainly through a speci c query. Below are the concepts key to the discussion that follows. Query: A text string a user types into the search box
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Fake News Detection on Social Media: A Data …
www.kdd.orgFake News Detection on Social Media: A Data Mining Perspective Kai Shuy, Amy Slivaz, Suhang Wangy, Jiliang Tang \, and Huan Liuy yComputer Science & Engineering, Arizona State University, Tempe, AZ, USA
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Structural Deep Network Embedding - SIGKDD
www.kdd.orgStructural Deep Network Embedding Daixin Wang1, Peng Cui1, Wenwu Zhu1 1Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University. Beijing, China dxwang0826@gmail.com,cuip@tsinghua.edu.cn,wwzhu@tsinghua.edu.cn
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XGBoost: A Scalable Tree Boosting System
www.kdd.orggradient tree boosting [10]1 is one technique that shines in many applications. Tree boosting has been shown to give state-of-the-art results on many standard classi cation benchmarks [16]. LambdaMART [5], a variant of tree boost-ing for ranking, achieves state-of-the-art result for ranking 1Gradient tree boosting is also known as gradient boosting
Graph Convolutional Matrix Completion
www.kdd.orgThe decoder model is a pairwise decoder Aˇ = д(Z), which takes pairs of node embeddings (zi,zj)and predicts entries Aˇ ... Graph Convolutional Matrix Completion KDD’18 Deep Learning Day, August 2018, London, UK.
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“Why Should I Trust You?” Explaining the Predictions of ...
www.kdd.orgargue that explaining predictions is an important aspect in getting humans to trust and use machine learning e ectively, if the explanations are faithful and intelligible. The process of explaining individual predictions is illus-trated in Figure 1. It is clear that a doctor is much better positioned to make a decision with the help of a model if
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Collaborative Knowledge Base Embedding for Recommender …
www.kdd.orgnetwork embedding method, termed as TransR, to extract items’ structural representations by considering the heterogeneity of both nodes and relationships. We apply stacked denoising auto-encoders and stacked convolutional auto-encoders, which are two types of deep learning based embedding techniques, to extract items’ tex-
Fake News Detection on Social Media: A Data Mining …
www.kdd.orgFake News Detection on Social Media: A Data Mining Perspective Kai Shuy, Amy Slivaz, Suhang Wangy, Jiliang Tang \, and Huan Liuy yComputer Science & Engineering, Arizona State University, Tempe, AZ, USA zCharles River Analytics, Cambridge, MA, USA \Computer Science & Engineering, Michigan State University, East Lansing, MI, USA
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