8 citations · 12 across the 6 of their papers we have counts for
6 papers
A Survey on LLM-powered Agents for Recommender Systems
Qiyao Peng, Hongtao Liu, Hua Huang +2
Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainab…
ULMRec: User-centric Large Language Model for Sequential Recommendation
Minglai Shao, Hua Huang, Qiyao Peng +1
Recent advances in Large Language Models (LLMs) have demonstrated promising performance in sequential recommendation tasks, leveraging their superior language understanding capabil…
Review-LLM: Harnessing Large Language Models for Personalized Review Generation
Qiyao Peng, Hongtao Liu, Hongyan Xu +3
Product review generation is an important task in recommender systems, which could provide explanation and persuasiveness for the recommendation. Recently, Large Language Models (L…
Adaptation Speed Analysis for Fairness-aware Causal Models
Yujie Lin, Chen Zhao, Minglai Shao +2
For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the trai…
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs
Dong Li, Wenjun Wang, Minglai Shao +1
As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-ce…
Stacked Kernel Network
Shuai Zhang, Jianxin Li, Pengtao Xie +4
Kernel methods are powerful tools to capture nonlinear patterns behind data. They implicitly learn high (even infinite) dimensional nonlinear features in the Reproducing Kernel Hil…