111 citations · 801 across the 59 of their papers we have counts for
59 papers
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
Hung Vinh Tran, Tong Chen, Quoc Viet Hung Nguyen +3
Since the creation of the Web, recommender systems (RSs) have been an indispensable mechanism in information filtering. State-of-the-art RSs primarily depend on categorical feature…
Lightweight Embeddings for Graph Collaborative Filtering
Xurong Liang, Tong Chen, Lizhen Cui +3
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item…
On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm
Hongzhi Yin, Tong Chen, Liang Qu +1
Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry. However, traditional clou…
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion
Lijian Chen, Wei Yuan, Tong Chen +3
Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences. Whi…
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution
Yuting Sun, Guansong Pang, Guanhua Ye +3
The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a…
Budgeted Embedding Table For Recommender Systems
Yunke Qu, Tong Chen, Quoc Viet Hung Nguyen +1
At the heart of contemporary recommender systems (RSs) are latent factor models that provide quality recommendation experience to users. These models use embedding vectors, which a…