187 results on '"Lichan Hong"'
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2. Leveraging LLM Reasoning Enhances Personalized Recommender Systems.
3. LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views.
4. Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems.
5. Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model.
6. How to Train Data-Efficient LLMs.
7. Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN).
8. Improving Training Stability for Multitask Ranking Models in Recommender Systems.
9. Multitask Ranking System for Immersive Feed and No More Clicks: A Case Study of Short-Form Video Recommendation.
10. Online Matching: A Real-time Bandit System for Large-scale Recommendations.
11. Efficient Data Representation Learning in Google-scale Systems.
12. HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer.
13. Distributionally-robust Recommendations for Improving Worst-case User Experience.
14. Can Small Heads Help? Understanding and Improving Multi-Task Generalization.
15. Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems.
16. Recommender Systems with Generative Retrieval.
17. Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction.
18. Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems.
19. Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication.
20. Better Generalization with Semantic IDs: A case study in Ranking for Recommendations.
21. Density Weighting for Multi-Interest Personalized Recommendation.
22. DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning.
23. A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation.
24. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems.
25. Learning to Embed Categorical Features without Embedding Tables for Recommendation.
26. Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model.
27. Self-supervised Learning for Large-scale Item Recommendations.
28. Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems.
29. Off-policy Learning in Two-stage Recommender Systems.
30. Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations.
31. Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems.
32. Improving Multi-Task Generalization via Regularizing Spurious Correlation.
33. Fairness in Recommendation Ranking through Pairwise Comparisons.
34. Recommending what video to watch next: a multitask ranking system.
35. Sampling-bias-corrected neural modeling for large corpus item recommendations.
36. SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning.
37. Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN).
38. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts.
39. TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks.
40. Efficient Training on Very Large Corpora via Gramian Estimation.
41. Self-supervised Learning for Deep Models in Recommendations.
42. Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems.
43. A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation.
44. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems.
45. Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model.
46. Deep Hash Embedding for Large-Vocab Categorical Feature Representations.
47. Small Towers Make Big Differences.
48. Evaluation and Refinement of Clustered Search Results with the Crowd.
49. Wide & Deep Learning for Recommender Systems.
50. AppGrouper: Knowledge-based Interactive Clustering Tool for App Search Results.
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