All Stories

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