29 citations · 31 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Understanding Embedding Scaling in Collaborative Filtering
Yicheng He, Zhou Kaiyu, Haoyue Bai +2
Scaling recommendation models into large recommendation models has become one of the most widely discussed topics. Recent efforts focus on components beyond the scaling embedding d…
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…
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…