5 papers
MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction
Shiwen Shen, Xiru Huang, Liang Luo +32
Not all clicks are equal. Industrial ads ranking decouples conversion probability into click-through rate (CTR) and post-click conversion rate (CVR), yet treats every click as the…
TALAN: Task-Aligned Latent Adaptation Networks for Targeted Post-Training of Large Language Models
Chengkai Zhang, Ziteng Liu, Junpu Wang +4
Targeted post-training aims to improve reasoning, math, and code without degrading strengths. Low-rank adapters are efficient but task-global; activation interventions are input-aw…
Improving Large-Scale Recommender Systems with Auxiliary Learning
Mertcan Cokbas, Ziteng Liu, Zeyi Tao +10
Training large-scale recommendation models under a single global objective implicitly assumes homogeneity across user populations. However, real-world data are composites of hetero…
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…
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
Xuewei Wang, Qiang Jin, Shengyu Huang +10
Dividing ads ranking system into retrieval, early, and final stages is a common practice in large scale ads recommendation to balance the efficiency and accuracy. The early stage r…