78 citations · 124 across the 35 of their papers we have counts for
7 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…
Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights
Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai +8
Time series data is ubiquitous across various domains such as finance, healthcare, and manufacturing, but their properties can vary significantly depending on the domain they origi…
An Efficient Content-based Time Series Retrieval System
Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai +9
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, health…
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…
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…