1 citations · 2 across the 5 of their papers we have counts for
6 papers
Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models
Tianrui Song, Wen-Shuo Chao, Hao Liu
Implicit feedback, employed in training recommender systems, unavoidably confronts noise due to factors such as misclicks and position bias. Previous studies have attempted to iden…
Bag of Tricks for Inference-time Computation of LLM Reasoning
Fan Liu, Wenshuo Chao, Naiqiang Tan +1
With the advancement of large language models (LLMs), solving complex reasoning tasks has gained increasing attention. Inference-time computation methods (e.g., Best-of-N, beam sea…
Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation
Tianrui Song, Wenshuo Chao, Hao Liu
Implicit feedback, often used to build recommender systems, unavoidably confronts noise due to factors such as misclicks and position bias. Previous studies have attempted to allev…
Harnessing Large Language Models for Text-Rich Sequential Recommendation
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu +2
Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textua…
Make Large Language Model a Better Ranker
Wen-Shuo Chao, Zhi Zheng, Hengshu Zhu +1
Large Language Models (LLMs) demonstrate robust capabilities across various fields, leading to a paradigm shift in LLM-enhanced Recommender System (RS). Research to date focuses on…
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
Wenshuo Chao, Zhaopeng Qiu, Likang Wu +4
The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain…