9 citations · 10 across the 4 of their papers we have counts for
8 papers
ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation
Yuxin Chen, Liang Luo, Buyun Zhang +44
Modern recommendation models gain prediction quality by scaling feature-interaction and sequence modules, but production cost constraints cap how far systems can scale. In this wor…
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation
Yihua Zhang, Mingfu Liang, Jiyan Yang +11
Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.…
Implicit Turn-Wise Policy Optimization for Proactive User-LLM Interaction
Haoyu Wang, Yuxin Chen, Liang Luo +3
Multi-turn human-AI collaboration is fundamental to deploying interactive services such as adaptive tutoring, conversational recommendation, and professional consultation. However,…
Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations
Liang Luo, Yuxin Chen, Zhengyu Zhang +39
The rapidly evolving landscape of products, surfaces, policies, and regulations poses significant challenges for deploying state-of-the-art recommendation models at industry scale,…
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Mingfu Liang, Xi Liu, Rong Jin +104
Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommenda…
InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
Zhichen Zeng, Xiaolong Liu, Mengyue Hang +25
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous informati…