2 citations · 5 across the 5 of their papers we have counts for
5 papers
Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts
Yunke Qu, Liang Qu, Tong Chen +2
Streaming recommender systems (SRSs) are widely deployed in real-world applications, where user interests shift and new items arrive over time. As a result, effectively capturing u…
Sparser Training for On-Device Recommendation Systems
Yunke Qu, Liang Qu, Tong Chen +3
Recommender systems often rely on large embedding tables that map users and items to dense vectors of uniform size, leading to substantial memory consumption and inefficiencies. Th…
Scalable Dynamic Embedding Size Search for Streaming Recommendation
Yunke Qu, Liang Qu, Tong Chen +3
Recommender systems typically represent users and items by learning their embeddings, which are usually set to uniform dimensions and dominate the model parameters. However, real-w…
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…
Continuous Input Embedding Size Search For Recommender Systems
Yunke Qu, Tong Chen, Xiangyu Zhao +3
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-val…