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8 papers · 1 filter
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering
Huiyuan Chen, Vivian Lai, Hongye Jin +3
Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to generate augmentation-invariant embeddings by maximizing the Mutual Informa…
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
Dongyu Zhang, Liang Wang, Xin Dai +7
Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, seq…
Hessian-aware Quantized Node Embeddings for Recommendation
Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai +4
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in recommender systems. Nevertheless, the process of searching and ranking from a large item corpus usually…
Tackling Diverse Minorities in Imbalanced Classification
Kwei-Herng Lai, Daochen Zha, Huiyuan Chen +5
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbal…
Adversarial Collaborative Filtering for Free
Huiyuan Chen, Xiaoting Li, Vivian Lai +5
Collaborative Filtering (CF) has been successfully used to help users discover the items of interest. Nevertheless, existing CF methods suffer from noisy data issue, which negative…
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…