Training Foundation Improvements for Closeup Recommendation Ranker

Training Foundation Improvements for Closeup Recommendation Ranker

摘要

Pinterest’s mission is- to bring everyone the inspiration to create a life they love. The closeup team helps with this mission by providing a feed of relevant and context-and-user-aware recommendations when a Pinner closes up on any Pin.

The recommendations are powered by innovative and cutting-edge machine learning technologies. We have published a detailed blog post of its modeling architecture. While adopting the newest architectures improves a model’s capabilities, building a solid training foundation stabilizes the model and further up-levels the model’s potential.

Training foundations cover a lot of aspects, from training preparation (training data logging, feature freshness, sampling strategies, hyperparameter tuning, etc), to training efficiency optimization (distributed training, model refreshes, GPU training, etc), to post training validation (offline replay, etc).