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Collaborative Knowledge Base Embedding for Recommender …

Collaborative Knowledge Base Embedding forRecommender SystemsFuzheng Zhang , Nicholas Jing Yuan , Defu Lian , Xing Xie ,Wei-Ying Ma Microsoft Research Big Data Research Center, University of Electronic Science and Technology of different recommendation techniques, Collaborative fil-tering usually suffer from limited performance due to the sparsityof user-item interactions. To address the issues, auxiliary informa-tion is usually used to boost the performance. Due to the rapidcollection of information on the web, the Knowledge base providesheterogeneous information including both structured and unstruc-tured data with different semantics, which can be consumed by var-ious applications.

network 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-

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Structural Deep Network Embedding - SIGKDD

www.kdd.org

Structural 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

  Network, Structural, Deep, Embedding, Structural deep network embedding

Graph Convolutional Matrix Completion

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The 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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Deep Crossing: Web-Scale Modeling without Manually …

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Manually crafted combinatorial features have been the \se-cret sauce" behind many successful models. For web-scale applications, however, the variety and volume of features make these manually crafted features expensive to create, maintain, and deploy. This paper proposes the Deep Cross-ing model which is a deep neural network that automatically

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Fake News Detection on Social Media: A Data …

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Fake 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

  Social, Media, States, Data, Perspective, Mining, News, Detection, News detection on social media, A data, A data mining perspective

XGBoost: A Scalable Tree Boosting System

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gradient 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

  Boost, Boosting, Derating, Gradient boosting, Boost ing

“Why Should I Trust You?” Explaining the Predictions of ...

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argue 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

  Trust, Explaining, Prediction, Trust you, Explaining the predictions of

Fake News Detection on Social Media: A Data Mining …

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Fake 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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Structural Deep Network Embedding - SIGKDD

www.kdd.org

network structure well and are robust to sparse networks. In summary, the contributions of this paper are listed as follows: We propose a Structural Deep Network Embedding method, namely SDNE, to perform network embedding. The method is able to map the data to a highly non-linear latent space to preserve the network structure and is robust to ...

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Deep Learning on Graphs - Michigan State University

cse.msu.edu

4.2.2 Preserving Structural Role 86 4.2.3 Preserving Node Status 89 ... ing traditional graph embedding, modern graph embedding, and deep learn-ing on graphs. As the first generation of graph representation learning, tra- ... social network analysis, GNNs result in state-of-the-art performance and bring

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Heterogeneous Graph Neural Network

www3.nd.edu

neural network architecture with two modules to aggregate feature information of those sampled neighboring nodes. The first module encodes “deep” feature interactions of heterogeneous contents and generates content embedding for each node. The second module aggregates content (attribute) embeddings of different neighboring

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Exploring Cross-Image Pixel Contrast for Semantic …

openaccess.thecvf.com

tures during segmentation network training [40,2,86]. Basically, these segmentation models (excluding [37]) utilize deep architectures to project image pixels into a highly non-linear embedding space (Fig.1(c)). However, they typically learn the embedding space that only makes use of “local” context around pixel samples (i.e., pixel de-

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Representation Learning on Graphs: Methods and Applications

www-cs.stanford.edu

encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction.

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DeepWalk: Online Learning of Social Representations

perozzi.net

is used on Zachary’s Karate network [44] to generate a la-tent representation in R2. Note the correspondence between community structure in the input graph and the embedding. Vertex colors represent a modularity-based clustering of the input graph. 1. INTRODUCTION The sparsity of a network representation is both a strength and a weakness.

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DeepWalk: Online Learning of Social Representations

www.cse.fau.edu

network classi cation tasks for social networks such as Blog-Catalog, Flickr, and YouTube. Our results show that Deep-Walk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk’s representations can pro-vide F 1 scores up to 10% higher than competing methods

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arXiv:1706.02216v4 [cs.SI] 10 Sep 2018

arxiv.org

inductive node embedding. Unlike embedding approaches that are based on matrix factorization, we leverage node features (e.g., text attributes, node profile information, node degrees) in order to learn an embedding function that generalizes to unseen nodes. By incorporating node features in the

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A Survey on Heterogeneous Graph Embedding: Methods ...

arxiv.org

embedding (i.e., heterogeneous graph representation learn-ing), aiming to learn a function that maps input space into a lower-dimension space while preserving the hetero-geneous structure and semantics, has drawn considerable attentions in recent years. Although there have been ample studies of embedding technology on homogeneous graphs

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