11 citations · 11 across the 4 of their papers we have counts for
4 papers
Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation
Shuo Chang, Yueqi Wang, Zihuan Diao +7
Recommender systems rely heavily on heterogeneous behavioral feedback to infer user preference. Although abundant, these signals are imperfect measurements: the same observed behav…
LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
Boyuan Long, Yueqi Wang, Hiloni Mehta +10
This paper presents a case study on deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content…
LLMs for User Interest Exploration in Large-scale Recommendation Systems
Jianling Wang, Haokai Lu, Yifan Liu +9
Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel u…
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
Yueqi Wang, Yoni Halpern, Shuo Chang +9
Sequential recommenders have been widely used in industry due to their strength in modeling user preferences. While these models excel at learning a user's positive interests, less…