3 papers
cs.IR2026
Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Xiao Lin, Zhicheng Tang, Weilin Cong +14
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long…
cs.IR2024
ERCache: An Efficient and Reliable Caching Framework for Large-Scale User Representations in Meta's Ads System
Fang Zhou, Yaning Huang, Dong Liang +21
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and s…
cs.IR2024
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
Wei Zhang, Dai Li, Chen Liang +17
Effective user representations are pivotal in personalized advertising. However, stringent constraints on training throughput, serving latency, and memory, often limit the complexi…