3 citations · 3 across the 4 of their papers we have counts for
5 papers
Do Reviews Matter for Recommendations in the Era of Large Language Models?
Chee Heng Tan, Huiying Zheng, Jing Wang +5
With the advent of large language models (LLMs), the landscape of recommender systems is undergoing a significant transformation. Traditionally, user reviews have served as a criti…
MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources
Sicong Leng, Jing Wang, Jiaxi Li +12
Large multimodal reasoning models have achieved rapid progress, but their advancement is constrained by two major limitations: the absence of open, large-scale, high-quality long c…
Error Analyses of Auto-Regressive Video Diffusion Models: A Unified Framework
Jing Wang, Fengzhuo Zhang, Xiaoli Li +5
Auto-Regressive Video Diffusion Models (AR-VDMs) have shown strong capabilities in generating long, photorealistic videos, but suffer from two key limitations: (i) history forgetti…
Hesitation and Tolerance in Recommender Systems
Kuan Zou, Aixin Sun, Yitong Ji +5
Users' interactions with recommender systems often involve more than simple acceptance or rejection. We highlight two overlooked states: hesitation, when people deliberate without…
LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation
Hongke Zhao, Songming Zheng, Likang Wu +2
The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensiv…