This article discusses how Faire uses graph neural networks (GNNs) to improve personalized product recommendations in its marketplace. It details the challenges of traditional recommendation systems and explains how GNNs model relationships between retailers and products to surface relevant items. The approach involves building a bipartite engagement graph and optimizing embeddings for better accuracy.

Facebook Reels enhanced its video recommendations by implementing a User True Interest Survey that directly collects user feedback on content relevance. This approach helps surface niche content, boosts user engagement, and addresses challenges like data sparsity and bias.

Pinterest has developed TransActV2, a new model that enhances personalized recommendations by utilizing over 16,000 user actions, allowing for long-term behavior modeling and improved ranking predictions. Key innovations include a Next Action Loss function for better forecasting and scalable deployment solutions, resulting in significant improvements in user engagement metrics. The model demonstrates substantial gains in both offline and online performance, setting a new benchmark for user sequence modeling in recommendation systems.

Pinterest has developed a user journey framework to enhance its recommendation system by understanding users' long-term goals and interests. This approach utilizes dynamic keyword extraction and clustering to create personalized journeys, which have significantly improved user engagement through journey-aware notifications. The system focuses on flexibility, leveraging existing data and models, while continuously evolving based on user behaviors and feedback.