16th ACM International Conference on Web Search and Data Mining, Singapore 2023

57 Lectures · Feb 27, 2023

About

WSDM (pronounced "wisdom") is one of the premier conferences on web-inspired research involving search and data mining.

WSDM is a highly selective conference that includes invited talks, as well as refereed full papers. WSDM publishes original, high-quality papers related to search and data mining on the Web and the Social Web, with an emphasis on practical yet principled novel models of search and data mining, algorithm design and analysis, economic implications, and in-depth experimental analysis of accuracy and performance.

Uploaded videos:

Keynote talks

57:38

Towards Autonomous Driving

Mar 17, 2023

29 Views

Keynote

01:00:07

Beyond-Accuracy Goals, Again

Mar 17, 2023

11 Views

Keynote

29:47

Learning to Understand Audio and Multimodal Content

Mar 17, 2023

10 Views

Keynote

Opening

14:40

Conference Opening

Mar 17, 2023

9 Views

Opening

Session #1: Social Issues in Data Mining

13:16

Local Edge Dynamics and Opinion Polarization

Mar 17, 2023

19 Views

16th ACM International Conference on Web Search and Data Mining, Singapore 2023

About

Uploaded videos:

Keynote talks

Towards Autonomous Driving

Beyond-Accuracy Goals, Again

Learning to Understand Audio and Multimodal Content

Opening

Conference Opening

Session #1: Social Issues in Data Mining

Local Edge Dynamics and Opinion Polarization

Never Too Late to Learn: Regularizing Gender Bias in Coreference Resolution

Marginal-Certainty-aware Fair Ranking Algorithm

Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Dis...

Mining User-aware Multi-Relations for Fake News Detection in Large Scale Online ...

BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Grap...

Session #2: Recommender Systems I

Simplifying Graph-based Collaborative Filtering for Recommendation

A Personalized Neighborhood-based Model for Within-basket Recommendation in Groc...

Relation Preference oriented High-order Sampling for Recommendation

Towards Universal Cross-Domain Recommendation

Self-Supervised Group Graph Collaborative Filtering for Group Recommendation

Disentangled Negative Sampling for Collaborative Filtering

Session #3: Graph Neural Networks

Minimum Entropy Principle Guided Graph Neural Networks

Global Counterfactual Explainer for Graph Neural Networks

Effective Graph Kernels for Evolving Functional Brain Networks

Self-Supervised Graph Structure Refinement for Graph Neural Networks

Learning to Distill Graph Neural Networks

MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Di...

Session #4: Best of WSDM 2023

Learning Stance Embeddings from Signed Social Graphs

Efficiently Leveraging Multi-level User Intent for Session-based Recommendation ...

Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning

Knowledge Enhancement for Contrastive Multi-Behavior Recommendation

Test of Time Award Talk

Session #5: Recommender Systems II

Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation

Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recomm...

Range Restricted Route Recommendation Based on Spatial Keyword

Meta Policy Learning for Cold-Start Conversational Recommendation

A Causal View for Item-level Effect of Recommendation on User Preference

Counterfactual Collaborative Reasoning

Session #6: Learning

NGAME: Negative Mining-aware Mini-batching for Extreme Classification

Reliable Decision from Multiple Subtasks through Threshold Optimization: Content...

Simultaneous Linear Multi-view Attributed Graph Representation Learning and Clus...

Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Inter...

Few-shot Node Classification with Extremely Weak Supervision

A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework

DeMEtRIS: Counting (near)-Cliques by Crawling

Session #7: Graph Mining

Interpretable Research Interest Shift Detection with Temporal Heterogeneous Grap...

Alleviating Structural Distribution Shift in Graph Anomaly Detection

Self-supervised Graph Representation Learning for Black Market Account Detection

GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Acc...

Session #8: Recommendation and Learning

One for All, All for One: Learning and Transferring User Embeddings for Cross-Do...

Federated Unlearning for On-Device Recommendation

S2TUL: A Semi-Supervised Framework for Trajectory-User Linking

DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement For...

Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation

Multi-Intention Oriented Contrastive Learning for Sequential Recommendation

Session #9: Language Models and Text Mining

Visual Matching Is Enough for Scene Text Retrieval

Can Pre-trained Language Models Understand Chinese Humor?

Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts

Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive...

AGREE: Aligning Cross-Modal Entities for Image-Text Retrieval Upon Vision-Langua...

WSDM Business Lunch

WSDM Business Lunch