20 citations · 66 across the 26 of their papers we have counts for
27 papers
Breaking the Top- Barrier: Advancing Top- Ranking Metrics Optimization in Recommender Systems
Weiqin Yang, Jiawei Chen, Shengjia Zhang +5
In the realm of recommender systems (RS), Top- ranking metrics such as NDCG@ are the gold standard for evaluating recommendation performance. However, during the training of…
Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
Shengjia Zhang, Jiawei Chen, Changdong Li +5
Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective…
Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
Chao Wang, Yue Zheng, Yujing Zhang +5
In a single-slot recommendation system, users are only exposed to one item at a time, and the system cannot collect user feedback on multiple items simultaneously. Therefore, only…
MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
Bohao Wang, Feng Liu, Jiawei Chen +7
Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS). Given the fundament…
Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
Sirui Chen, Shen Han, Jiawei Chen +6
Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of R…
Uncertainty-Aware Graph Structure Learning
Shen Han, Zhiyao Zhou, Jiawei Chen +6
Graph Neural Networks (GNNs) have become a prominent approach for learning from graph-structured data. However, their effectiveness can be significantly compromised when the graph…
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
Weiqin Yang, Jiawei Chen, Xin Xin +5
Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant lim…
Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Power
Zhe Wang, Tianjian Zhao, Zhen Zhang +5
Dynamic Graph Neural Networks (DyGNNs) have garnered increasing research attention for learning representations on evolving graphs. Despite their effectiveness, the limited express…
LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation
Bohao Wang, Feng Liu, Changwang Zhang +8
Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy i…
Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding
Zhe Wang, Sheng Zhou, Jiawei Chen +5
Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling compl…
Motif-driven Subgraph Structure Learning for Graph Classification
Zhiyao Zhou, Sheng Zhou, Bochao Mao +5
To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstr…
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Siyi Lin, Chongming Gao, Jiawei Chen +5
Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this…
Distributionally Robust Graph-based Recommendation System
Bohao Wang, Jiawei Chen, Changdong Li +6
With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy of…
Knowledge Translation: A New Pathway for Model Compression
Wujie Sun, Defang Chen, Jiawei Chen +3
Deep learning has witnessed significant advancements in recent years at the cost of increasing training, inference, and model storage overhead. While existing model compression met…
CDR: Conservative Doubly Robust Learning for Debiased Recommendation
ZiJie Song, JiaWei Chen, Sheng Zhou +4
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a…
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Zhiyao Zhou, Sheng Zhou, Bochao Mao +7
Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node at…
Robust Sequence Networked Submodular Maximization
Qihao Shi, Bingyang Fu, Can Wang +4
In this paper, we study the \underline{R}obust \underline{o}ptimization for \underline{se}quence \underline{Net}worked \underline{s}ubmodular maximization (RoseNets) problem. We in…
Accelerating Diffusion Sampling with Classifier-based Feature Distillation
Wujie Sun, Defang Chen, Can Wang +3
Although diffusion model has shown great potential for generating higher quality images than GANs, slow sampling speed hinders its wide application in practice. Progressive distill…
GReS: Graphical Cross-domain Recommendation for Supply Chain Platform
Zhiwen Jing, Ziliang Zhao, Yang Feng +6
Supply Chain Platforms (SCPs) provide downstream industries with numerous raw materials. Compared with traditional e-commerce platforms, data in SCPs is more sparse due to limited…
Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding
Yichen Liu, Jiawei Chen, Defang Chen +3
Knowledge Graph Embedding (KGE), which projects entities and relations into continuous vector spaces, has garnered significant attention. Although high-dimensional KGE methods offe…
Knowledge Distillation with the Reused Teacher Classifier
Defang Chen, Jian-Ping Mei, Hailin Zhang +3
Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance. For this purpose, various ap…
Online Adversarial Knowledge Distillation for Graph Neural Networks
Can Wang, Zhe Wang, Defang Chen +3
Knowledge distillation, a technique recently gaining popularity for enhancing model generalization in Convolutional Neural Networks (CNNs), operates under the assumption that both…
Cross-Layer Distillation with Semantic Calibration
Defang Chen, Jian-Ping Mei, Yuan Zhang +3
Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model. Recently, feature-map based variants expl…
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
Jiawei Chen, Chengquan Jiang, Can Wang +5
Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instanc…