121 citations · 324 across the 17 of their papers we have counts for
17 papers
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
Peiyan Zhang, Yuchen Yan, Xi Zhang +5
In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional…
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Peilin Zhou, You-Liang Huang, Yueqi Xie +4
Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrasti…
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs
Peiyan Zhang, Chaozhuo Li, Liying Kang +4
We investigate node representation learning on text-attributed graphs (TAGs), where nodes are associated with text information. Although recent studies on graph neural networks (GN…
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Peiyan Zhang, Yuchen Yan, Xi Zhang +4
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-…
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Peiyan Zhang, Haoyang Liu, Chaozhuo Li +3
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in…
Attention Calibration for Transformer-based Sequential Recommendation
Peilin Zhou, Qichen Ye, Yueqi Xie +5
Transformer-based sequential recommendation (SR) has been booming in recent years, with the self-attention mechanism as its key component. Self-attention has been widely believed t…
Continual Learning on Dynamic Graphs via Parameter Isolation
Peiyan Zhang, Yuchen Yan, Chaozhuo Li +4
Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetti…
A Survey on Incremental Update for Neural Recommender Systems
Peiyan Zhang, Sunghun Kim
Recommender Systems (RS) aim to provide personalized suggestions of items for users against consumer over-choice. Although extensive research has been conducted to address differen…
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
Yueqi Xie, Jingqi Gao, Peilin Zhou +5
Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglect…
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
Renjie Pi, Weizhong Zhang, Yueqi Xie +4
The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, whi…
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Yueqi Xie, Weizhong Zhang, Renjie Pi +4
Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…
Equivariant Contrastive Learning for Sequential Recommendation
Peilin Zhou, Jingqi Gao, Yueqi Xie +5
Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augm…
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting
Juyong Jiang, Binqing Wu, Ling Chen +2
Traffic forecasting is an essential problem in urban planning and computing. The complex dynamic spatial-temporal dependencies among traffic objects (e.g., sensors and road segment…
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Peiyan Zhang, Jiayan Guo, Chaozhuo Li +6
Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various…
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
Jiayan Guo, Peiyan Zhang, Chaozhuo Li +3
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions. Recently, there has been an increasing interest in modeling the user preferen…
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Juyong Jiang, Peiyan Zhang, Yingtao Luo +6
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions. A pivotal challenge in user modeling for SR lies in the inherent variability o…
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training
Juyong Jiang, Peiyan Zhang, Yingtao Luo +6
Sequential recommendation systems are integral to discerning temporal user preferences. Yet, the task of learning from abbreviated user interaction sequences poses a notable challe…