52 citations · 84 across the 10 of their papers we have counts for
10 papers
Selective Mixup for Debiasing Question Selection in Computerized Adaptive Testing
Mi Tian, Kun Zhang, Fei Liu +6
Computerized Adaptive Testing (CAT) is a widely used technology for evaluating learners' proficiency in online education platforms. By leveraging prior estimates of proficiency to…
Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning
Miaomiao Cai, Min Hou, Lei Chen +4
Collaborative Filtering~(CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based…
Boosting Explainability through Selective Rationalization in Pre-trained Language Models
Libing Yuan, Shuaibo Hu, Kui Yu +1
The widespread application of pre-trained language models (PLMs) in natural language processing (NLP) has led to increasing concerns about their explainability. Selective rationali…
Graph Bottlenecked Social Recommendation
Yonghui Yang, Le Wu, Zihan Wang +3
With the emergence of social networks, social recommendation has become an essential technique for personalized services. Recently, graph-based social recommendations have shown pr…
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
Miaomiao Cai, Lei Chen, Yifan Wang +5
Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to…
Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis
Dacao Zhang, Kun Zhang, Le Wu +3
Cognitive Diagnosis~(CD), which leverages students and exercise data to predict students' proficiency levels on different knowledge concepts, is one of fundamental components in In…
It is Never Too Late to Mend: Separate Learning for Multimedia Recommendation
Zhuangzhuang He, Zihan Wang, Yonghui Yang +2
Multimedia recommendation, which incorporates various modalities (e.g., images, texts, etc.) into user or item representation to improve recommendation quality, and self-supervised…
Double Correction Framework for Denoising Recommendation
Zhuangzhuang He, Yifan Wang, Yonghui Yang +6
As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in…
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation
Haoyue Bai, Le Wu, Min Hou +5
Multimedia-based recommendation provides personalized item suggestions by learning the content preferences of users. With the proliferation of digital devices and APPs, a huge numb…
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
Peijie Sun, Le Wu, Kun Zhang +2
While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to intr…