mlsanityData to 7 Oct 2026

Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)

arXiv 2409.16594 ↗First posted 25 Sep 2024cs.IR cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages from retrieved content based on input queries. However, traditional LTR models encounter two principal obstacles that lead to suboptimal performance: (1) the lack of well-annotated query-webpage pairs with ranking scores covering a diverse range of search query popularities, which hampers their ability to address queries across the popularity spectrum, and (2) inadequately trained models that fail to induce generalized representations for LTR, resulting in overfitting. To address these challenges, we propose a emphulineGenerative ulineSemi-ulineSupervised ulinePre-trained (GS2P) LTR model. We conduct extensive offline experiments on both a publicly available dataset and a real-world dataset collected from a large-scale search engine. Furthermore, we deploy GS2P in a large-scale web search engine with realistic traffic, where we observe significant improvements in the real-world application.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 46 of its 97 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

46 of 97 references.

References · 46

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 919
  4. 2020DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systemscited by 326
  5. 2021Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolationcited by 130
  6. 2023Dual Aggregation Transformer for Image Super-Resolutioncited by 124
  7. 2013Introducing LETOR 4.0 Datasetscited by 120
  8. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  9. 2024AgentReview: Exploring Peer Review Dynamics with LLM Agentscited by 92
  10. 2023Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queriescited by 82
  11. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  12. 2024Robustifying Safety-Aligned Large Language Models through Clean Data Curationcited by 43
34 more
  1. 2021NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sortingcited by 39
  2. 2024Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformerscited by 35
  3. 2024MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platformscited by 26
  4. 2024CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variablescited by 25
  5. 2020Context-Aware Learning to Rank with Self-Attentioncited by 22
  6. 2023ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecastingcited by 21
  7. 2023Model Extraction Attacks Revisitedcited by 21
  8. 2024Enhance Image-to-Image Generation with LLaVA-generated Promptscited by 20
  9. 2024MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimizationcited by 20
  10. 2023Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Codingcited by 18
  11. 2022Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanationcited by 16
  12. 2024WildfireGPT: Tailored Large Language Model for Wildfire Analysiscited by 15
  13. 2024Towards Rationality in Language and Multimodal Agents: A Surveycited by 15
  14. 2024XMeCap: Meme Caption Generation with Sub-Image Adaptabilitycited by 14
  15. 2024Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigmcited by 13
  16. 2024Can Pre-trained Language Models Understand Chinese Humor?cited by 13
  17. 2024Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Modelscited by 13
  18. 2024Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approachcited by 12
  19. 2020Learning to Rank for Active Learning: A Listwise Approachcited by 11
  20. 2022Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizescited by 10
  21. 2021OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learningcited by 9
  22. 2021Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communicationcited by 9
  23. 2023GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropycited by 9
  24. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  25. 2024Portfolio Optimization with Robust Covariance and Conditional Value-at-Risk Constraintscited by 9
  26. 2024Let Community Rules Be Reflected in Online Content Moderationcited by 9
  27. 2024Recent Advancement of Emotion Cognition in Large Language Modelscited by 9
  28. 2024Do Large Language Models have Problem-Solving Capability under Incomplete Information Scenarios?cited by 7
  29. 2024HOTVCOM: Generating Buzzworthy Comments for Videoscited by 7
  30. 2024Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networkscited by 5
  31. 2021Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Datacited by 4
  32. 2023SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectorycited by 4
  33. 2024Towards Automated Data Sciences with Natural Language and SageCopilot: Practices and Lessons Learnedcited by 3
  34. 2024Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-trainingcited by 2

Built on by

Not cited yet.