Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness

no code implementations • 21 Feb 2024 • Yuying Zhao, Minghua Xu, Huiyuan Chen, Yuzhong Chen, Yiwei Cai, Rashidul Islam, Yu Wang, Tyler Derr

Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests.

Fairness Recommendation Systems

Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation

no code implementations • 20 Aug 2023 • Vivian Lai, Huiyuan Chen, Chin-Chia Michael Yeh, Minghua Xu, Yiwei Cai, Hao Yang

Despite their success, Transformer-based models often require the optimization of a large number of parameters, making them difficult to train from sparse data in sequential recommendation.

Self-Supervised Learning Sequential Recommendation