Publications (15)
Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale
Jieming Di, Xiaoyu Chen, Ying She +21
Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retraining -- resulting in long itera…
Decoupled Entity Representation Learning for Pinterest Ads Ranking
Jie Liu, Yinrui Li, Jiankai Sun +12
In this paper, we introduce a novel framework following an upstream-downstream paradigm to construct user and item (Pin) embeddings from diverse data sources, which are essential f…
Fine-Tuned LLM as a Complementary Predictor Improving Ads System
Hui Yang, Daiwei He, Kevin Jiang +20
Recommendation systems power engagement and monetization across feeds, ads, and short-video platforms, but translating the latest advances in Large Language Models into Recommendat…
Privacy Preserving Conversion Modeling in Data Clean Room
Kungang Li, Xiangyi Chen, Ling Leng +3
In the realm of online advertising, accurately predicting the conversion rate (CVR) is crucial for enhancing advertising efficiency and user satisfaction. This paper addresses the…
On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction
Jinfeng Zhuang, Yinrui Li, Runze Su +14
The predictions of click through rate (CTR) and conversion rate (CVR) play a crucial role in the success of ad-recommendation systems. A Deep Hierarchical Ensemble Network (DHEN) h…
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
Xiangyu Wang, Yawen He, Shivendra Pratap Singh +12
Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditional approaches mitigate this v…