350 citations · 1.9k across the 82 of their papers we have counts for
85 papers
Graph-based Alignment and Uniformity for Recommendation
Liangwei Yang, Zhiwei Liu, Chen Wang +4
Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hy…
Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
Xi Wu, Liangwei Yang, Jibing Gong +4
Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based mod…
Contrastive Graph Clustering in Curvature Spaces
Li Sun, Feiyang Wang, Junda Ye +2
Graph clustering is a longstanding research topic, and has achieved remarkable success with the deep learning methods in recent years. Nevertheless, we observe that several importa…
Hierarchical State Abstraction Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li +3
State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations. Nevertheless, recent approaches focus…
Graph Collaborative Signals Denoising and Augmentation for Recommendation
Ziwei Fan, Ke Xu, Zhang Dong +3
Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, whic…
A Post-Training Framework for Improving Heterogeneous Graph Neural Networks
Cheng Yang, Xumeng Gong, Chuan Shi +1
Recent years have witnessed the success of heterogeneous graph neural networks (HGNNs) in modeling heterogeneous information networks (HINs). In this paper, we focus on the benchma…
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Dongcheng Zou, Hao Peng, Xiang Huang +5
Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather th…
Graph-based Village Level Poverty Identification
Jing Ma, Liangwei Yang, Qiong Feng +2
Poverty status identification is the first obstacle to eradicating poverty. Village-level poverty identification is very challenging due to the arduous field investigation and insu…
Weakly Supervised Anomaly Detection: A Survey
Minqi Jiang, Chaochuan Hou, Ao Zheng +6
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.…
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Xixi Wu, Yun Xiong, Yao Zhang +4
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation tas…
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou, Qian Li, Chen Li +16
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis +7
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from w…
Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation
Xiaohan Li, Yuqing Liu, Zheng Liu +1
Session-based Recommendation (SBR) is to predict users' next interested items based on their previous browsing sessions. Existing methods model sessions as graphs or sequences to e…
Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
Ziwei Fan, Zhiwei Liu, Hao Peng +1
Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mut…
Self-organization Preserved Graph Structure Learning with Principle of Relevant Information
Qingyun Sun, Jianxin Li, Beining Yang +3
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption ca…
Localized Contrastive Learning on Graphs
Hengrui Zhang, Qitian Wu, Yu Wang +3
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its qu…
Internet of Behaviors: A Survey
Jiayi Sun, Wensheng Gan, Han-Chieh Chao +2
The Internet of Behavior is a research theme that aims to analyze human behavior data on the Internet from the perspective of behavioral psychology, obtain insights about human beh…
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Liangwei Yang, Shengjie Wang, Yunzhe Tao +4
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-…
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Due to the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the ranking-base…
MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System
Liangwei Yang, Shen Wang, Jibing Gong +4
Knowledge graph (KG) enhanced recommendation has demonstrated improved performance in the recommendation system (RecSys) and attracted considerable research interest. Recently the…
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Xuming Hu, Shiao Meng, Chenwei Zhang +4
Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NE…
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
Li Sun, Junda Ye, Hao Peng +2
Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior…
Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?
Byung-Hak Kim, Zhongfen Deng, Philip S. Yu +1
The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) c…
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer
Ziwei Fan, Zhiwei Liu, Chen Wang +3
Sequential Recommendation (SR) models user dynamics and predicts the next preferred items based on the user history. Existing SR methods model the 'was interacted before' item-item…