activity
20192024
most citedSelf-supervised Learning on Graphs: Deep Insights and New Direction

111 citations · 330 across the 42 of their papers we have counts for

collaborators

49 papers

cs.CV2024

GUIOdyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices

Quanfeng Lu, Wenqi Shao, Zitao Liu +7

Autonomous Graphical User Interface (GUI) navigation agents can enhance user experience in communication, entertainment, and productivity by streamlining workflows and reducing man…

cs.CY2024★ 1 cited

A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models

Hengyuan Zhang, Zitao Liu, Chenming Shang +2

Knowledge tracing (KT) plays a crucial role in predicting students' future performance by analyzing their historical learning processes. Deep neural networks (DNNs) have shown grea…

cs.CY2024

Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning

Hengyuan Zhang, Zitao Liu, Shuyan Huang +3

Knowledge tracing (KT) aims to estimate student's knowledge mastery based on their historical interactions. Recently, the deep learning based KT (DLKT) approaches have achieved imp…

cs.LG2023★ 2 cited

Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Jiahao Chen, Zitao Liu, Shuyan Huang +2

Knowledge tracing (KT) is a crucial technique to predict students' future performance by observing their historical learning processes. Due to the powerful representation ability o…

cs.LG2023★ 26 cited

simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing

Zitao Liu, Qiongqiong Liu, Jiahao Chen +2

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works pres…

cs.IR2023★ 2 cited

Fairly Adaptive Negative Sampling for Recommendations

Xiao Chen, Wenqi Fan, Jingfan Chen +4

Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (…