4 citations · 8 across the 13 of their papers we have counts for
13 papers
Behavior Pattern Mining-based Multi-Behavior Recommendation
Haojie Li, Zhiyong Cheng, Xu Yu +3
Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models th…
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects
Pengfei Ding, Yan Wang, Guanfeng Liu
Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention from both academia and industry because prevailing studies on heterogeneous graphs often suffer from l…
Adaptive Hypergraph Network for Trust Prediction
Rongwei Xu, Guanfeng Liu, Yan Wang +3
Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users.…
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
Pengfei Ding, Yan Wang, Guanfeng Liu +2
Heterogeneous graph few-shot learning (HGFL) has been developed to address the label sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and edges…
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
Yongjing Hao, Pengpeng Zhao, Junhua Fang +5
Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective a…
Cross-heterogeneity Graph Few-shot Learning
Pengfei Ding, Yan Wang, Guanfeng Liu
In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and…