5 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.IR2024
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness
Yuying Zhao, Minghua Xu, Huiyuan Chen +5
Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests. However, the complexity and nuanced…
cs.IR2023★ 5 cited
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation
Vivian Lai, Huiyuan Chen, Chin-Chia Michael Yeh +3
Transformer and its variants are a powerful class of architectures for sequential recommendation, owing to their ability of capturing a user's dynamic interests from their past int…