All Stories

  1. The Future of Personalized Universal Assistant
  2. Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems
  3. Improving Data Efficiency for Recommenders and LLMs
  4. Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems
  5. Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale Recommenders
  6. Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations
  7. Serving Large User Sequence Models in Large Scale Applications
  8. Co-optimize Content Generation and Consumption in a Large Scale Video Recommendation System
  9. Multi-Task Neural Linear Bandit for Exploration in Recommender Systems
  10. Large Language Models as Data Augmenters for Cold-Start Item Recommendation
  11. Cluster Anchor Regularization to Alleviate Popularity Bias in Recommender Systems
  12. Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses
  13. Long-Term Value of Exploration: Measurements, Findings and Algorithms
  14. Multitask Ranking System for Immersive Feed and No More Clicks: A Case Study of Short-Form Video Recommendation
  15. Online Matching: A Real-time Bandit System for Large-scale Recommendations
  16. Efficient Data Representation Learning in Google-scale Systems
  17. Improving Training Stability for Multitask Ranking Models in Recommender Systems
  18. Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
  19. Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
  20. HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
  21. Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems
  22. Latent User Intent Modeling for Sequential Recommenders
  23. Off-Policy Actor-critic for Recommender Systems
  24. Surrogate for Long-Term User Experience in Recommender Systems
  25. Distributionally-robust Recommendations for Improving Worst-case User Experience
  26. Learning to Augment for Casual User Recommendation
  27. Can Small Heads Help? Understanding and Improving Multi-Task Generalization
  28. Multi-Resolution Attention for Personalized Item Search
  29. Self-supervised Learning for Large-scale Item Recommendations
  30. Values of User Exploration in Recommender Systems
  31. Learning to Embed Categorical Features without Embedding Tables for Recommendation
  32. Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
  33. Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective
  34. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
  35. Towards Content Provider Aware Recommender Systems
  36. A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
  37. User Response Models to Improve a REINFORCE Recommender System
  38. Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
  39. Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
  40. Deconfounding User Satisfaction Estimation from Response Rate Bias
  41. End-to-End Deep Attentive Personalized Item Retrieval for Online Content-sharing Platforms
  42. Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations
  43. Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
  44. Off-policy Learning in Two-stage Recommender Systems
  45. Recommending what video to watch next
  46. Sampling-bias-corrected neural modeling for large corpus item recommendations
  47. Quantifying Long Range Dependence in Language and User Behavior to improve RNNs
  48. Fairness in Recommendation Ranking through Pairwise Comparisons
  49. Towards Neural Mixture Recommender for Long Range Dependent User Sequences
  50. Top-K Off-Policy Correction for a REINFORCE Recommender System
  51. Practical Diversified Recommendations on YouTube with Determinantal Point Processes
  52. Q&R
  53. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
  54. Evaluation and Refinement of Clustered Search Results with the Crowd
  55. The Case for Learned Index Structures
  56. Latent Cross
  57. Design for Searching & Finding
  58. Instant foodie