7 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.DC2025
FLAME: A Serving System Optimized for Large-Scale Generative Recommendation with Efficiency
Xianwen Guo, Bin Huang, Xiaomeng Wu +6
Generative recommendation (GR) models possess greater scaling power compared to traditional deep learning recommendation models (DLRMs), yet they also impose a tremendous increase…
cs.IR2025★ 7 cited
Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation
Shijia Wang, Tianpei Ouyang, Qiang Xiao +5
In music recommendation systems, multimodal interest learning is pivotal, which allows the model to capture nuanced preferences, including textual elements such as lyrics and vario…
cs.IR2025
Climber: Toward Efficient Scaling Laws for Large Recommendation Models
Songpei Xu, Shijia Wang, Da Guo +5
Transformer-based generative models have achieved remarkable success across domains with various scaling law manifestations. However, our extensive experiments reveal persistent ch…