58 citations · 59 across the 5 of their papers we have counts for
9 papers
Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?
Mingqiao Zhang, Qiyao Peng, Yinghui Wang +2
The expanding integration of Large Language Models (LLMs) into recommender systems poses critical challenges to evaluation reliability. This paper identifies and investigates a pre…
FRONTIER-RevRec: A Large-scale Dataset for Reviewer Recommendation
Qiyao Peng, Chen Wang, Yinghui Wang +3
Reviewer recommendation is a critical task for enhancing the efficiency of academic publishing workflows. However, research in this area has been persistently hindered by the lack…
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…
Beyond Fixed Length: Bucket Pre-training is All You Need
Qing Yang, Qiyao Peng, Hongtao Liu +3
Large Language Models (LLMs) have demonstrated exceptional performance across various tasks, with pre-training stage serving as the cornerstone of their capabilities. However, the…