8 papers
RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems
Haoran Ling, Yuecheng Li, Zeyu Song +5
Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes. While LLM-based agents can…
Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
Siyun Yang, Shixiao Yang, Jian Wang +6
In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction. Observed clicks reflect a mixture of…
CS3: Efficient Online Capability Synergy for Two-Tower Recommendation
Lixiang Wang, Shaoyun Shi, Peng Wang +2
To balance effectiveness and efficiency in recommender systems, multi-stage pipelines employ lightweight two-tower models for large-scale candidate retrieval. However, their isolat…
CS3: Efficient Online Capability Synergy for Two-Tower Recommendation
Lixiang Wang, Shaoyun Shi, Peng Wang +2
To balance effectiveness and efficiency in recommender systems, multi-stage pipelines commonly use lightweight two-tower models for large-scale candidate retrieval. However, the is…
Generative Recommendation for Large-Scale Advertising
Ben Xue, Dan Liu, Lixiang Wang +27
Generative recommendation has recently attracted widespread attention in industry due to its potential for scaling and stronger model capacity. However, deploying real-time generat…
AlignGR: Unified Multi-Level Alignment for LLM-based Generative Recommendation
Wencai Ye, Mingjie Sun, Shuhang Chen +2
Large Language Models (LLMs) demonstrate significant advantages in leveraging structured world knowledge and multi-step reasoning capabilities. However, fundamental challenges aris…