7 papers · 1 filter
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…
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…
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…
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…