3 papers
cs.LG2025
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…
cs.IR2024
Improving feature interactions at Pinterest under industry constraints
Siddarth Malreddy, Matthew Lawhon, Usha Amrutha Nookala +2
Adopting advances in recommendation systems is often challenging in industrial settings due to unique constraints. This paper aims to highlight these constraints through the lens o…
cs.CV2022
Using Multiple Self-Supervised Tasks Improves Model Robustness
Matthew Lawhon, Chengzhi Mao, Junfeng Yang
Deep networks achieve state-of-the-art performance on computer vision tasks, yet they fail under adversarial attacks that are imperceptible to humans. In this paper, we propose a n…