2.9k citations · 2.9k across the 5 of their papers we have counts for
5 papers
PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform
Xiangyi Chen, Kousik Rajesh, Matthew Lawhon +9
User activity sequences have emerged as one of the most important signals in recommender systems. We present a foundational model, PinFM, for understanding user activity sequences…
TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation
Xue Xia, Saurabh Vishwas Joshi, Kousik Rajesh +6
Modeling user action sequences has become a popular focus in industrial recommendation system research, particularly for Click-Through Rate (CTR) prediction tasks. However, industr…
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
Xue Xia, Pong Eksombatchai, Nikil Pancha +7
Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems. Traditional m…
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Jiaxuan You, Yichen Wang, Aditya Pal +3
Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches of…
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying, Ruining He, Kaifeng Chen +3
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods pract…