4 papers
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…
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…
Multi-Faceted Large Embedding Tables for Pinterest Ads Ranking
Runze Su, Jiayin Jin, Jiacheng Li +16
Large embedding tables are indispensable in modern recommendation systems, thanks to their ability to effectively capture and memorize intricate details of interactions among diver…
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}…