12 citations · 12 across the 4 of their papers we have counts for
4 papers
Aligning Large Language Models with Recommendation Knowledge
Yuwei Cao, Nikhil Mehta, Xinyang Yi +5
Large language models (LLMs) have recently been used as backbones for recommender systems. However, their performance often lags behind conventional methods in standard tasks like…
Density Weighting for Multi-Interest Personalized Recommendation
Nikhil Mehta, Anima Singh, Xinyang Yi +3
Using multiple user representations (MUR) to model user behavior instead of a single user representation (SUR) has been shown to improve personalization in recommendation systems.…
Online Matching: A Real-time Bandit System for Large-scale Recommendations
Xinyang Yi, Shao-Chuan Wang, Ruining He +6
The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. Whi…
Improving Training Stability for Multitask Ranking Models in Recommender Systems
Jiaxi Tang, Yoel Drori, Daryl Chang +6
Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we foun…