AM-GCN: Adaptive Multi-channel Graph Convolutional Networks 文中提出在复杂网络中，现有的GCN方法融合节点属性信息...
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论文笔记之AM-GCN: Adaptive Multi-channel Graph Convolutional Networks -
论文笔记之Deep Anomaly Detection on Attributed Networks
Deep Anomaly Detection on Attributed Networks 提出了DOMINANT(Deep Anomaly Detection on Att...
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论文笔记之Time Series Shapelets: A New Primitive for Data MiningTime Series Shapelets: A New Primitive for Data Mining 对于时序数据，提出了一种新的特征，名为shapelet。shap...
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论文笔记之Dynamic Heterogeneous Graph Neural Network for Real-time Event PredictionDynamic Heterogeneous Graph Neural Network for Real-time Event Prediction 本文是滴滴发在KDD202...
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论文笔记之GPT-GNN: Generative Pre-Training of Graph Neural NetworksGPT-GNN: Generative Pre-Training of Graph Neural Networks 文中指出训练GNN需要大量和任务对应的标注数据，这在很多时...
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论文笔记之Deep Neural Networks for YouTube RecommendationsDeep Neural Networks for YouTube Recommendations 文中把整个推荐过程分成两个步骤：•deep candidate genera...
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论文笔记之xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender SystemsxDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems 目...
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论文笔记之Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction 目标...
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论文笔记之A Convolutional Click Prediction ModelA Convolutional Click Prediction Model 目标：CTR预估 Convolution Layer 对于有n个元素的输入，先做embeddin...
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论文笔记之Deep Interest Evolution Network for Click-Through Rate PredictionDeep Interest Evolution Network for Click-Through Rate Prediction 目标：CTR预估文中指出以前的模型忽略了用...
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论文笔记之Deep Interest Network for Click-Through Rate PredictionDeep Interest Network for Click-Through Rate Prediction 本文指出，在基于embedding和MLP的模型中，用户的各种...
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论文笔记之Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attentio...Attentional Factorization Machines: Learning the Weight of Feature Interactions via Att...
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论文笔记之Neural Factorization Machines for Sparse Predictive AnalyticsNeural Factorization Machines for Sparse Predictive Analytics 文中提到了对于稀疏特征交互的处理。FM以线性的方式...
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论文笔记之DeepFM: A Factorization-Machine based Neural Network for CTR PredictionDeepFM: A Factorization-Machine based Neural Network for CTR Prediction 目标：CTR预估文中指出以前的...
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论文笔记之Deep Learning over Multi-field Categorical Data – A Case Study on UserDeep Learning over Multi-field Categorical Data – A Case Study on User 目标：CTR预估。本文提出了FN...
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论文笔记之Deep & Cross Network for Ad Click PredictionsDeep & Cross Network for Ad Click Predictions 目标：CTR预估。本文提出了Deep & Cross Network(DCN)能够...
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论文笔记之Wide & Deep Learning for Recommender SystemsWide & Deep Learning for Recommender Systems 文中指出推荐系统的一个挑战是同时实现memorization和generalizat...
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论文笔记之Product-based Neural Networks for User Response PredictionProduct-based Neural Networks for User Response Prediction 目标：CTR预估。文中指出LR，GBDT，FM虽然在工业...
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论文笔记之Neural Collaborative FilteringNeural Collaborative Filtering 文中指出虽然之前有一些工作使用deep learning来解决推荐问题，但这些工作基本上使用deep learn...
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论文笔记之Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial FeaturesDeep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features 文中开篇讲...