3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
ML-DCN: Masked Low-Rank Deep Crossing Network Towards Scalable Ads Click-through Rate Prediction at Pinterest
Jiacheng Li, Yixiong Meng, Yi wu +7
Deep learning recommendation systems rely on feature interaction modules to model complex user-item relationships across sparse categorical and dense features. In large-scale ad ra…
Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads
Jiayin Jin, Erika Sun, Zhimeng Pan +10
Graph Neural Networks (GNN) have been extensively applied to industry recommendation systems, as seen in models like GraphSage\cite{GraphSage}, TwHIM\cite{TwHIM}, LiGNN\cite{LiGNN}…
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…
The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion
Andrew Qiu, Shubham Barhate, Hin Wai Lui +7
Deep learning for conversion prediction has found widespread applications in online advertising. These models have become more complex as they are trained to jointly predict multip…
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…