1 citations · 1 across the 8 of their papers we have counts for
8 papers
SAHC-NS: Structure-Aware and Hardness-Calibrated Negative Sampling for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +3
Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods…
Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection
Bochen Lin, Jianxiang Yu, Jiayi Wu +3
Graph anomaly detection (GAD) on text-attributed graphs (TAGs) is vital for applications such as fraud detection and academic integrity verification. Existing approaches generally…
TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
The negative sampling strategy can effectively train collaborative filtering (CF) recommendation models based on implicit feedback by constructing positive and negative samples. Ho…
A Topology-Aware Positive Sample Set Construction and Feature Optimization Method in Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Negative sampling strategies are widely used in implicit collaborative filtering to address issues like data sparsity and class imbalance. However, these methods often introduce fa…
A Simple yet Effective Negative Sampling Plugin for Constructing Positive Sample Pairs in Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Most implicit collaborative filtering (CF) models are trained with negative sampling, where existing work designs sophisticated strategies for high-quality negatives while largely…
Dual-Tree LLM-Enhanced Negative Sampling for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Negative sampling is a pivotal technique in implicit collaborative filtering (CF) recommendation, enabling efficient and effective training by contrasting observed interactions wit…
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
Xunkai Li, Zhengyu Wu, Jiayi Wu +4
With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration…
Effective Bug Detection in Graph Database Engines: An LLM-based Approach
Jiayi Wu, Zhengyu Wu, Ronghua Li +2
Graph database engines play a pivotal role in efficiently storing and managing graph data across various domains, including bioinformatics, knowledge graphs, and recommender system…