All Stories

  1. How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
  2. Adapting LLMs for Personalized Evaluation of Explanations for Recommendations: A Meta-Learning Approach based on MAML
  3. Addressing Personalized Bias for Unbiased Learning to Rank
  4. Dense Retrieval for Aggregated Search
  5. CLUE: Using Large Language Models for Judging Document Usefulness in Web Search Evaluation
  6. FinS-Pilot: A Benchmark for Online Financial RAG System
  7. MGIPF: Multi-Granularity Interest Prediction Framework for Personalized Recommendation
  8. Distributionally Robust Optimization for Unbiased Learning to Rank
  9. Exploring Human-Like Thinking in Search Simulations with Large Language Models
  10. A Flexible User Study Platform for Generative Information Retrieval
  11. Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study
  12. Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
  13. TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy
  14. MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
  15. Investigating Users' Search Behavior and Outcome with ChatGPT in Learning-oriented Search Tasks
  16. Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval
  17. Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information
  18. Scaling Laws For Dense Retrieval
  19. USimAgent: Large Language Models for Simulating Search Users
  20. An Integrated Data Processing Framework for Pretraining Foundation Models
  21. CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models
  22. An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models
  23. An Intent Taxonomy of Legal Case Retrieval
  24. Improving First-stage Retrieval of Point-of-Interest Search by Pre-training Models
  25. Understanding the Multi-vector Dense Retrieval Models
  26. Constructing Tree-based Index for Efficient and Effective Dense Retrieval
  27. Session Search with Pre-trained Graph Classification Model
  28. A Passage-Level Reading Behavior Model for Mobile Search
  29. User Behavior Simulation for Search Result Re-ranking
  30. Understanding Relevance Judgments in Legal Case Retrieval
  31. Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
  32. 4th Workshop on Deep Learning Practice and Theory for High-Dimensional Sparse and Imbalanced Data with KDD 2022
  33. Axiomatically Regularized Pre-training for Ad hoc Search
  34. Webformer
  35. Generating Clarifying Questions with Web Search Results
  36. Learning Probabilistic Box Embeddings for Effective and Efficient Ranking
  37. Global or Local: Constructing Personalized Click Models for Web Search
  38. A Cooperative Neural Information Retrieval Pipeline with Knowledge Enhanced Automatic Query Reformulation
  39. Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
  40. Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
  41. POSSCORE
  42. Incorporating Query Reformulating Behavior into Web Search Evaluation
  43. Evaluating Relevance Judgments with Pairwise Discriminative Power
  44. A Hybrid Framework for Session Context Modeling
  45. Optimizing Dense Retrieval Model Training with Hard Negatives
  46. Investigating User Behavior in Legal Case Retrieval
  47. Investigating Session Search Behavior with Knowledge Graphs
  48. Unbiased Learning to Rank
  49. Constructing a Comparison-based Click Model for Web Search
  50. Towards a Better Understanding of Query Reformulation Behavior in Web Search
  51. Topic-enhanced knowledge-aware retrieval model for diverse relevance estimation
  52. Challenges in designing a brain-machine search interface
  53. Neural Logic Reasoning
  54. Learning Better Representations for Neural Information Retrieval with Graph Information
  55. Preference-based Evaluation Metrics for Web Image Search
  56. Models Versus Satisfaction
  57. Cascade or Recency
  58. Investigating Reading Behavior in Fine-grained Relevance Judgment
  59. An Analysis of BERT in Document Ranking
  60. Modeling User Behavior for Vertical Search: Images, Apps and Products
  61. Leveraging Passage-level Cumulative Gain for Document Ranking
  62. "Revisiting information retrieval tasks with user behavior models" by Yiqun Liu and Jiaxin Mao with Martin Vesely as coordinator
  63. TianGong-ST
  64. Improving Web Image Search with Contextual Information
  65. Investigating the Learning Process in Job Search
  66. Context-Aware Ranking by Constructing a Virtual Environment for Reinforcement Learning
  67. Investigating the Reliability of Click Models
  68. Search Result Reranking with Visual and Structure Information Sources
  69. Teach Machine How to Read
  70. Towards Context-Aware Evaluation for Image Search
  71. Investigating Passage-level Relevance and Its Role in Document-level Relevance Judgment
  72. Human Behavior Inspired Machine Reading Comprehension
  73. SIGIR 2019 Tutorial on Explainable Recommendation and Search
  74. WWW’19 Tutorial on Explainable Recommendation and Search
  75. Grid-based Evaluation Metrics for Web Image Search
  76. Understanding Reading Attention Distribution during Relevance Judgement
  77. Unbiased Learning to Rank
  78. How Does Domain Expertise Affect Users’ Search Interaction and Outcome in Exploratory Search?
  79. Constructing Click Models for Mobile Search
  80. Towards Designing Better Session Search Evaluation Metrics
  81. ACM SIGIR Student Liaison Program
  82. "Satisfaction with Failure" or "Unsatisfied Success"
  83. Understanding and Predicting Usefulness Judgment in Web Search
  84. When does Relevance Mean Usefulness and User Satisfaction in Web Search?