SIGKDD(KDD) 2017 论文列表
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017.
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Optimized Cost per Click in Taobao Display Advertising.
STAR: A System for Ticket Analysis and Resolution.
Resolving the Bias in Electronic Medical Records.
Contextual Spatial Outlier Detection with Metric Learning.
A Taxi Order Dispatch Model based On Combinatorial Optimization.
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns.
DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks.
Predicting Optimal Facility Location without Customer Locations.
A Data-driven Process Recommender Framework.
Visual Search at eBay.
Local Algorithm for User Action Prediction Towards Display Ads.
Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data.
Formative Essay Feedback Using Predictive Scoring Models.
A Hybrid Framework for Text Modeling with Convolutional RNN.
Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration.
Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes.
Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention.
Automatic Application Identification from Billions of Files.
The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue.
Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach.
"The Leicester City Fairytale?": Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons.
Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines.
Collecting and Analyzing Millions of mHealth Data Streams.
Deep Design: Product Aesthetics for Heterogeneous Markets.
An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis.
Learning to Count Mosquitoes for the Sterile Insect Technique.
Embedding-based News Recommendation for Millions of Users.
RUSH!: Targeted Time-limited Coupons via Purchase Forecasts.
Internet Device Graphs.
Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks.
BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency.
Supporting Employer Name Normalization at both Entity and Cluster Level.
Discovering Enterprise Concepts Using Spreadsheet Tables.
Discovering Pollution Sources and Propagation Patterns in Urban Area.
Ad Serving with Multiple KPIs.
Finding Precursors to Anomalous Drop in Airspeed During a Flight's Takeoff.
Optimization Beyond Prediction: Prescriptive Price Optimization.
Large Scale Sentiment Learning with Limited Labels.
An Efficient Bandit Algorithm for Realtime Multivariate Optimization.
Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster.
Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem.
AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments.
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices.
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks.
Customer Lifetime Value Prediction Using Embeddings.
Real-Time Optimization of Web Publisher RTB Revenues.
Extremely Fast Decision Tree Mining for Evolving Data Streams.
Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity.
A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications.
Deep Embedding Forest: Forest-based Serving with Deep Embedding Features.
KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial.
A Quasi-experimental Estimate of the Impact of P2P Transportation Platforms on Urban Consumer Patterns.
No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods.
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution.
The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms.
Quick Access: Building a Smart Experience for Google Drive.
MOLIERE: Automatic Biomedical Hypothesis Generation System.
A Practical Exploration System for Search Advertising.
MARAS: Signaling Multi-Drug Adverse Reactions.
Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data.
Backpage and Bitcoin: Uncovering Human Traffickers.
Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says!
Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction.
Developing a Comprehensive Framework for Multimodal Feature Extraction.
Cascade Ranking for Operational E-commerce Search.
FLAP: An End-to-End Event Log Analysis Platform for System Management.
Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data.
PNP: Fast Path Ensemble Method for Movie Design.
Peeking at A/B Tests: Why it matters, and what to do about it.
HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network.
Predicting Clinical Outcomes Across Changing Electronic Health Record Systems.
Google Vizier: A Service for Black-Box Optimization.
GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources.
A Data Mining Framework for Valuing Large Portfolios of Variable Annuities.
Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning.
FIRST: Fast Interactive Attributed Subgraph Matching.
A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations.
A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments.
Estimation of Recent Ancestral Origins of Individuals on a Large Scale.
A Data Science Approach to Understanding Residential Water Contamination in Flint.
LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace.
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform.
Planning Bike Lanes based on Sharing-Bikes' Trajectories.
Luck is Hard to Beat: The Difficulty of Sports Prediction.
Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale.
Achieving Non-Discrimination in Data Release.
Visualizing Attributed Graphs via Terrain Metaphor.
LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity.
Inductive Semi-supervised Multi-Label Learning with Co-Training.
A Temporally Heterogeneous Survival Framework with Application to Social Behavior Dynamics.
Learning from Multiple Teacher Networks.
Small Batch or Large Batch?: Gaussian Walk with Rebound Can Teach.
Learning from Labeled and Unlabeled Vertices in Networks.
Multi-task Function-on-function Regression with Co-grouping Structured Sparsity.
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation.
Scalable Top-n Local Outlier Detection.
Distributed Local Outlier Detection in Big Data.
Convex Factorization Machine for Toxicogenomics Prediction.
Evaluating U.S. Electoral Representation with a Joint Statistical Model of Congressional Roll-Calls, Legislative Text, and Voter Registration Data.
Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates.
Retrospective Higher-Order Markov Processes for User Trails.
Structural Event Detection from Log Messages.
Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models.
Multi-Modality Disease Modeling via Collective Deep Matrix Factorization.
Adversary Resistant Deep Neural Networks with an Application to Malware Detection.
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users.
Optimized Risk Scores.
Construction of Directed 2K Graphs.
End-to-end Learning for Short Text Expansion.
Sparse Compositional Local Metric Learning.
PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency.
Relay-Linking Models for Prominence and Obsolescence in Evolving Networks.
Anomaly Detection in Streams with Extreme Value Theory.
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams.
ReasoNet: Learning to Stop Reading in Machine Comprehension.
When is a Network a Network?: Multi-Order Graphical Model Selection in Pathways and Temporal Networks.
Detecting Network Effects: Randomizing Over Randomized Experiments.
Inferring the Strength of Social Ties: A Community-Driven Approach.
An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance.
Automatic Synonym Discovery with Knowledge Bases.
Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting.
Let's See Your Digits: Anomalous-State Detection using Benford's Law.
Unsupervised Discovery of Drug Side-Effects from Heterogeneous Data Sources.
Functional Zone Based Hierarchical Demand Prediction For Bike System Expansion.
Point-of-Interest Demand Modeling with Human Mobility Patterns.
Distributed Multi-Task Relationship Learning.
A Context-aware Attention Network for Interactive Question Answering.
Prospecting the Career Development of Talents: A Survival Analysis Perspective.
Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites.
Statistical Emerging Pattern Mining with Multiple Testing Correction.
Federated Tensor Factorization for Computational Phenotyping.
MetaPAD: Meta Pattern Discovery from Massive Text Corpora.
Incremental Dual-memory LSTM in Land Cover Prediction.
SPOT: Sparse Optimal Transformations for High Dimensional Variable Selection and Exploratory Regression Analysis.
Recurrent Poisson Factorization for Temporal Recommendation.
Anarchists, Unite: Practical Entropy Approximation for Distributed Streams.
REMIX: Automated Exploration for Interactive Outlier Detection.
Revisiting Power-law Distributions in Spectra of Real World Networks.
Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks.
Algorithmic Decision Making and the Cost of Fairness.
GRAM: Graph-based Attention Model for Healthcare Representation Learning.
Unsupervised Feature Selection in Signed Social Networks.
On Sampling Strategies for Neural Network-based Collaborative Filtering.
Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization.
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection.
Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings.
Bolt: Accelerated Data Mining with Fast Vector Compression.
Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews.
Post Processing Recommender Systems for Diversity.
Tripoles: A New Class of Relationships in Time Series Data.
Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers.
Anomaly Detection with Robust Deep Autoencoders.
A Local Algorithm for Structure-Preserving Graph Cut.
Coresets for Kernel Regression.
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks.
Tracking the Dynamics in Crowdfunding.
Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling.
Graph Edge Partitioning via Neighborhood Heuristic.
TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams.
EmbedJoin: Efficient Edit Similarity Joins via Embeddings.
Weisfeiler-Lehman Neural Machine for Link Prediction.
Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity.
Local Higher-Order Graph Clustering.
PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification.
Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts.
HoORaYs: High-order Optimization of Rating Distance for Recommender Systems.
Large-scale Collaborative Ranking in Near-Linear Time.
FORA: Simple and Effective Approximate Single-Source Personalized PageRank.
Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes.
Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods.
Structural Deep Brain Network Mining.
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking.
AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification.
Scalable and Sustainable Deep Learning via Randomized Hashing.
Multi-Aspect Streaming Tensor Completion.
PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks.
On Finding Socially Tenuous Groups for Online Social Networks.
Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping.
Similarity Forests.
struc2vec: Learning Node Representations from Structural Identity.
SPARTan: Scalable PARAFAC2 for Large & Sparse Data.
Towards an Optimal Subspace for K-Means.
Discovering Reliable Approximate Functional Dependencies.
Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning.
Effective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams.
Discrete Content-aware Matrix Factorization.
Linearized GMM Kernels and Normalized Random Fourier Features.
Collaborative Variational Autoencoder for Recommender Systems.
Is the Whole Greater Than the Sum of Its Parts?
Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics.
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables.
Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing.
A Hierarchical Algorithm for Extreme Clustering.
Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines.
Accelerating Innovation Through Analogy Mining.
Efficient Correlated Topic Modeling with Topic Embedding.
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data.
Network Inference via the Time-Varying Graphical Lasso.
Clustering Individual Transactional Data for Masses of Users.
Groups-Keeping Solution Path Algorithm for Sparse Regression with Automatic Feature Grouping.
The Co-Evolution Model for Social Network Evolving and Opinion Migration.
Unsupervised P2P Rental Recommendations via Integer Programming.
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data.
Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks.
Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery.
Fast Enumeration of Large k-Plexes.
HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics.
A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection.
KATE: K-Competitive Autoencoder for Text.
Robust Top-k Multiclass SVM for Visual Category Recognition.
Patient Subtyping via Time-Aware LSTM Networks.
Unsupervised Network Discovery for Brain Imaging Data.
Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks.
Learning Certifiably Optimal Rule Lists.
The Future of Artificially Intelligent Assistants.
Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?
Spaceborne Data Enters the Mainstream.
Designing AI at Scale to Power Everyday Life.
Machine Learning Software in Practice: Quo Vadis?
Addressing Challenges with Big Data for Media Measurement.
Big Data in Climate: Opportunities and Challenges for Machine Learning.
Planning and Learning under Uncertainty: Theory and Practice.
It Takes More than Math and Engineering to Hit the Bullseye with Data.
Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management.
Industrial Machine Learning.
Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy.
More than the Sum of its Parts: Building Domino Data Lab.
Foreword to the Applied Data Science: Invited Talks Track at KDD-2017.
Three Principles of Data Science: Predictability, Stability and Computability.
The Future of Data Integration.
What's Fair?
