16 citations · 30 across the 6 of their papers we have counts for
6 papers
Unleashing the Power of Large Language Model for Denoising Recommendation
Shuyao Wang, Zhi Zheng, Yongduo Sui +1
Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve…
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Yuyang Ye, Zhi Zheng, Yishan Shen +6
Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converti…
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…
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
Shuyao Wang, Yongduo Sui, Jiancan Wu +2
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to p…
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…