All Stories

  1. User Welfare Optimization in Recommender Systems with Competing Content Creators
  2. Third Workshop on Personalization and Recommendations in Search (PaRiS)
  3. The Second Workshop on Large Language Models for Individuals, Groups, and Society
  4. Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems
  5. Retention Depolarization in Recommender System
  6. WSDM 2024 Workshop on Large Language Models for Individuals, Groups, and Society
  7. An End-to-End Solution for Spatial Inference in Smart Buildings
  8. Incentivizing Exploration in Linear Contextual Bandits under Information Gap
  9. Not Just Skipping: Understanding the Effect of Sponsored Content on Users' Decision-Making in Online Health Search
  10. Personalization and Recommendations in Search
  11. Meta Policy Learning for Cold-Start Conversational Recommendation
  12. Disentangled Representation for Diversified Recommendations
  13. Dynamic Global Sensitivity for Differentially Private Contextual Bandits
  14. Graph Structural Attack by Perturbing Spectral Distance
  15. Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback
  16. Emotion Recognition Robust to Indoor Environmental Distortions and Non-targeted Emotions Using Out-of-distribution Detection
  17. Learning Neural Ranking Models Online from Implicit User Feedback
  18. Comparative Explanations of Recommendations
  19. Graph-based Extractive Explainer for Recommendations
  20. Unbiased Graph Embedding with Biased Graph Observations
  21. Graph Embedding with Hierarchical Attentive Membership
  22. Towards semantic search in building sensor data
  23. Improve Learning from Crowds via Generative Augmentation
  24. Category-aware Collaborative Sequential Recommendation
  25. When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
  26. Interactive Information Retrieval with Bandit Feedback
  27. Understanding and Mitigating Bias in Online Health Search
  28. PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
  29. Explanation as a Defense of Recommendation
  30. Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction
  31. A monitoring, modeling, and interactive recommendation system for in-home caregivers
  32. Global and Local Differential Privacy for Collaborative Bandits
  33. Directional Multivariate Ranking
  34. Graph Attention Networks over Edge Content-Based Channels
  35. Learning by Exploration
  36. Accounting for Temporal Dynamics in Document Streams
  37. Active Collaborative Sensing for Energy Breakdown
  38. Factorization Bandits for Online Influence Maximization
  39. The FacT
  40. Context Attentive Document Ranking and Query Suggestion
  41. Variance Reduction in Gradient Exploration for Online Learning to Rank
  42. Dynamic Ensemble of Contextual Bandits to Satisfy Users' Changing Interests
  43. A Tree-Structured Neural Network Model for Household Energy Breakdown
  44. Learning Personalized Topical Compositions with Item Response Theory
  45. Modeling Sequential Online Interactive Behaviors with Temporal Point Process
  46. A Scalable Solution for Rule-Based Part-of-Speech Tagging on Novel Hardware Accelerators
  47. Efficient Exploration of Gradient Space for Online Learning to Rank
  48. Hide-n-Seek
  49. Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search
  50. Learning Contextual Bandits in a Non-stationary Environment
  51. Returning is Believing
  52. Modeling Student Learning Styles in MOOCs
  53. Search, Mining, and Their Applications on Mobile Devices
  54. Accounting for the Correspondence in Commented Data
  55. Clustered Model Adaption for Personalized Sentiment Analysis
  56. Learning Hidden Features for Contextual Bandits
  57. Contextual Bandits in a Collaborative Environment
  58. Topic Model based Privacy Protection in Personalized Web Search
  59. The Building Adapter
  60. Clustering-based Active Learning on Sensor Type Classification in Buildings