3 citations · 3 across the 12 of their papers we have counts for
12 papers
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…
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…
Memento: Personalized RAG-Style Long-Retention Data Scaling for META Ads Recommendation
Xiaoyu Chen, Ruichen Wang, Jieming Di +21
Modeling of long history data suffers from long-context window attention dilution, system efficiency and catastrophic forgetting problems, where naive linear scaling approach like…
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…
MTMD: A Multi-Task Multi-Domain Framework for Unified Ad Lightweight Ranking at Pinterest
Xiao Yang, Peifeng Yin, Abe Engle +2
The lightweight ad ranking layer, living after the retrieval stage and before the fine ranker, plays a critical role in the success of a cascaded ad recommendation system. Due to t…
Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
Xiao Yang, Mehdi Ben Ayed, Longyu Zhao +8
The ranking utility function in an ad recommender system, which linearly combines predictions of various business goals, plays a central role in balancing values across the platfor…
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}…
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…