103 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Rice UniversityUS4 papers
- University of Illinois Urbana-ChampaignUS3 papers
- The Ohio State UniversityUS2 papers
- Bellevue Hospital CenterUS1 paper
- Boston UniversityUS1 paper
- East Texas A&M UniversityUS1 paper
- Expedia Group (United States)US1 paper
- First Affiliated Hospital of Fujian Medical UniversityCN1 paper
- Fujian Medical UniversityCN1 paper
- Fuzhou UniversityCN1 paper
- Georgia Institute of TechnologyUS1 paper
- Linköping UniversitySE1 paper
9 papers · 1 filter
Masked Graph Transformer for Large-Scale Recommendation
Huiyuan Chen, Zhe Xu, Chin-Chia Michael Yeh +4
Graph Transformers have garnered significant attention for learning graph-structured data, thanks to their superb ability to capture long-range dependencies among nodes. However, t…
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…
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…
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…
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
Huiyuan Chen, Xiaoting Li, Kaixiong Zhou +4
There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability fo…
Denoising Self-attentive Sequential Recommendation
Huiyuan Chen, Yusan Lin, Menghai Pan +6
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies. This is mainly attributed to their unique self…
GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation
Aravind Sankar, Yanhong Wu, Yuhang Wu +3
We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent…
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
Adit Krishnan, Mahashweta Das, Mangesh Bendre +2
The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-c…