wsdm 2018 论文列表Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, February 5-9, 2018. |
IFUP: Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization.
GTA3 2018: Workshop on Graph Techniques for Adversarial Activity Analytics.
Workshop on Two-sided Marketplace Optimization: Search, Pricing, Matching & Growth.
MIS2: Misinformation and Misbehavior Mining on the Web.
LearnIR: WSDM 2018 Workshop on Learning from User Interactions.
HeteroNAM: International Workshop on Heterogeneous Networks Analysis and Mining.
First Workshop on Knowledge Base Construction, Mining and Reasoning.
The 5th International Workshop on Social Web for Disaster Management(SWDM'18): Collective Sensing, Trust, and Resilience in Global Crises.
Mining Knowledge Graphs From Text.
Athlytics: Winning in Sports with Data.
A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.
Network Science of Teams: Characterization, Prediction, and Optimization.
Tutorial on Metrics of User Engagement: Applications to News, Search and E-Commerce.
Neural Networks for Information Retrieval.
Differential Privacy for Information Retrieval.
Influence Maximization in Online Social Networks.
Collabot: Personalized Group Chat Summarization.
Supporting Large-scale Geographical Visualization in a Multi-granularity Way.
Conversational Semantic Search: Looking Beyond Web Search, Q&A and Dialog Systems.
Paul A. Crook Alex Marin Vipul Agarwal Samantha Anderson Ohyoung Jang Aliasgar Lanewala Karthik Tangirala Imed Zitouni
Percolator: Scalable Pattern Discovery in Dynamic Graphs.
Exploiting Human Mobility Patterns for Point-of-Interest Recommendation.
Engagement and Incentives in Online Community: Observational Data, Prediction Models, and Field Experiments.
Beyond Who and What: Data Driven Approaches for User Characterization.
Mining Twitter for Fine-Grained Political Opinion Polarity Classification, Ideology Detection and Sarcasm Detection.
Automatic Ranking of Information Retrieval Systems.
Connectivity in Complex Networks: Measures, Inference and Optimization.
Event Mining over Distributed Text Streams.
Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks.
Micro Behaviors: A New Perspective in E-commerce Recommender Systems.
Discrete Deep Learning for Fast Content-Aware Recommendation.
Neural Ranking Models with Multiple Document Fields.
Review-Aware Answer Prediction for Product-Related Questions Incorporating Aspects.
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce.
Dynamic Word Embeddings for Evolving Semantic Discovery.
OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms.
Why People Search for Images using Web Search Engines.
Indirect Supervision for Relation Extraction using Question-Answer Pairs.
Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate.
Customer Purchase Behavior Prediction from Payment Datasets.
A Path-constrained Framework for Discriminating Substitutable and Complementary Products in E-commerce.
Position Bias Estimation for Unbiased Learning to Rank in Personal Search.
A Unified Processing Paradigm for Interactive Location-based Web Search.
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction.
Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering.
sSketch: A Scalable Sketching Technique for PCA in the Cloud.
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding.
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction.
User Intent, Behaviour, and Perceived Satisfaction in Product Search.
Shortcutting Label Propagation for Distributed Connected Components.
Modeling Time to Open of Emails with a Latent State for User Engagement Level.
CrossFire: Cross Media Joint Friend and Item Recommendations.
Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration.
Peeling Bipartite Networks for Dense Subgraph Discovery.
Measuring the Latency of Depression Detection in Social Media.
FACH: Fast Algorithm for Detecting Cohesive Hierarchies of Communities in Large Networks.
Leveraging Implicit Contribution Amounts to Facilitate Microfinancing Requests.
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec.
DSANLS: Accelerating Distributed Nonnegative Matrix Factorization via Sketching.
Extreme Multi-label Learning with Label Features for Warm-start Tagging, Ranking & Recommendation.
Learning to Discover Domain-Specific Web Content.
Neural Personalized Ranking for Image Recommendation.
Fusing Diversity in Recommendations in Heterogeneous Information Networks.
Index Compression Using Byte-Aligned ANS Coding and Two-Dimensional Contexts.
Query Driven Algorithm Selection in Early Stage Retrieval.
Multi-Dimensional Network Embedding with Hierarchical Structure.
Inferring Dockless Shared Bike Distribution in New Cities.
Streaming Link Prediction on Dynamic Attributed Networks.
Bayesian Optimization for Optimizing Retrieval Systems.
Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter.
Web Search of Fashion Items with Multimodal Querying.
REV2: Fraudulent User Prediction in Rating Platforms.
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation.
Topic Chronicle Forest for Topic Discovery and Tracking.
Combating Crowdsourced Review Manipulators: A Neighborhood-Based Approach.
Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs.
Recommendation in Heterogeneous Information Networks Based on Generalized Random Walk Model and Bayesian Personalized Ranking.
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval.
Exploring Expert Cognition for Attributed Network Embedding.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction.
Who Will Share My Image?: Predicting the Content Diffusion Path in Online Social Networks.
Collaborative Filtering via Additive Ordinal Regression.
Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons.
Robust Transfer Learning for Cross-domain Collaborative Filtering Using Multiple Rating Patterns Approximation.
Identifying Informational vs. Conversational Questions on Community Question Answering Archives.
Care to Share?: Learning to Rank Personal Photos for Public Sharing.
Offline A/B Testing for Recommender Systems.
Unsubscription: A Simple Way to Ease Overload in Email.
Orienteering Algorithms for Generating Travel Itineraries.
User Profiling through Deep Multimodal Fusion.
Cognitive Biases in Crowdsourcing.
Predicting Audio Advertisement Quality.
Joint Generative-Discriminative Aggregation Model for Multi-Option Crowd Labels.
Demographics and Dynamics of Mechanical Turk Workers.
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search.
VISIR: Visual and Semantic Image Label Refinement.
Sequential Recommendation with User Memory Networks.
Improving Negative Sampling for Word Representation using Self-embedded Features.
Putting Data in the Driver's Seat: Optimizing Earnings for On-Demand Ride-Hailing.
Fast Coreset-based Diversity Maximization under Matroid Constraints.
Sketch 'Em All: Fast Approximate Similarity Search for Dynamic Data Streams.
Neural Graph Learning: Training Neural Networks Using Graphs.
Consistent Transformation of Ratio Metrics for Efficient Online Controlled Experiments.
Latent Cross: Making Use of Context in Recurrent Recommender Systems.
A Discrete Choice Model for Subset Selection.
Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images.
Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data.
Performance Analysis of a Privacy Constrained kNN Recommendation Using Data Sketches.
WSDM Cup 2018: Music Recommendation and Churn Prediction.
Scalable Algorithms in the Age of Big Data and Network Sciences: Characterization, Primitives, and Techniques.
From Search to Research: Direct Answers, Perspectives and Dialog.
Conversations, Machine Learning and Privacy: LinkedIn's Path Towards Transforming Interaction with Its Members.
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution.
On the Power of Massive Text Data.
A Call to Arms: Embrace Assistive AI Systems!