58 citations · 74 across the 10 of their papers we have counts for
11 papers
GNFormer: Gaussian Neural Field Transformer for Efficient Camera-based Occupancy Prediction
Guangcheng Chen, Li He, Hong Zhang
Camera-based occupancy prediction provides geometric and semantic scene understanding for robot perception and has been adopted in autonomous vehicle and UAV perception systems. Ga…
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…
PEPT: Expert Finding Meets Personalized Pre-training
Qiyao Peng, Hongyan Xu, Yinghui Wang +3
Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. T…
PUNR: Pre-training with User Behavior Modeling for News Recommendation
Guangyuan Ma, Hongtao Liu, Xing Wu +4
News recommendation aims to predict click behaviors based on user behaviors. How to effectively model the user representations is the key to recommending preferred news. Existing w…
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
Qianyu Guo, Sen Chen, Xiaofei Xie +6
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in archite…
NRPA: Neural Recommendation with Personalized Attention
Hongtao Liu, Fangzhao Wu, Wenjun Wang +4
Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, differe…