8 citations · 22 across the 8 of their papers we have counts for
8 papers
How Does Message Passing Improve Collaborative Filtering?
Mingxuan Ju, William Shiao, Zhichun Guo +4
Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods…
Improving Out-of-Vocabulary Handling in Recommendation Systems
William Shiao, Mingxuan Ju, Zhichun Guo +5
Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions…
GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method
Zubair Qazi, William Shiao, Evangelos E. Papalexakis
As natural language models like ChatGPT become increasingly prevalent in applications and services, the need for robust and accurate methods to detect their output is of paramount…
Node Duplication Improves Cold-start Link Prediction
Zhichun Guo, Tong Zhao, Yozen Liu +5
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show tha…
CARL-G: Clustering-Accelerated Representation Learning on Graphs
William Shiao, Uday Singh Saini, Yozen Liu +3
Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of…
Link Prediction with Non-Contrastive Learning
William Shiao, Zhichun Guo, Tong Zhao +3
A recent focal area in the space of graph neural networks (GNNs) is graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data. Not…
Linkless Link Prediction via Relational Distillation
Zhichun Guo, William Shiao, Shichang Zhang +4
Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood da…
FRAPPE: ast nk roximation with xplainable Features for Tensors
William Shiao, Evangelos E. Papalexakis
Tensor decompositions have proven to be effective in analyzing the structure of multidimensional data. However, most of these methods require a key parameter: the number of desired…