Papers for

advertising platform teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Multi task learning improves recommendation accuracy and satisfaction

Learned Cross-Task Relationships in Multi-Task Models

Abstract: We propose a framework that learns cross-task relationships in multi-task models by approximating the joint distribution of task labels through targeted pairwise relationships. This approach improves performance via transfer learning and enhances information extraction without the intractable complexity of modeling the full joint space. Although our framework applies to any multi-task system, we demonstrate its efficacy within YouTube's production recommendation systems. Experiments across the Notifications, Homepage, and Watch Next surfaces show improvements in both accuracy and user satisfaction metrics. Finally, we propose a workflow template to facilitate broader future implementation.

Wed 23 SeptArtificial Intelligence
The gist
Many online services try to do several related tasks at once, like recommending videos and sending notifications. The authors introduce a way to help computers understand how these tasks relate to each other by looking at pairs of tasks rather than everything at once. This approach makes the system better at learning and sharing useful information, improving recommendation performance. They tested their method on YouTube’s recommendations and found it made users happier with the suggestions they received.
Open → 2609.28776v1

Lightweight ranking heads speed up recommender system experiments

Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems

Abstract: Modern production-scale recommender systems rely on complex, multi-task ranking models. Introducing new prediction tasks into these massive systems often causes bottlenecks - it risks negative task conflicts with existing tasks, and can lead to long development and experimentation cycles due to the expensive retraining of backbone models and downstream models or tuning of reward combination formulas. To address the critical challenge of slow experimentation velocity, we introduce the Lightweight Ranking Heads (Light Heads) framework. Designed for continuous online learning environments, Light Heads enable the dynamic injection of new tasks into existing multi-task ranking models, effectively obviating the need for model cold-starting and retraining of backbone models. By utilizing stop-gradients and stateless daily training, this design strictly isolates new tasks, mitigating the risk of adverse task conflicts. Crucially, this framework uses a centralized configuration that allows Light Heads to be added to multiple models simultaneously, unblocking faster training data generation and co-training of downstream models. Successfully deployed at YouTube scale, this approach reduces the iteration cycle for multi-task experimentation from several weeks to days. In this paper, we detail the system architecture, analyze the training dynamics of stateless cold-started heads, compare their performance to full heads, and demonstrate how Light Heads have enabled the rapid A/B experimentation and deployment of new ranking tasks that yield measurable production value.

Mon 21 SeptMachine LearningArtificial Intelligence
The gist
Adding new tasks to big recommendation models is usually slow and tricky because it can interfere with existing tasks and requires retraining the whole system. The authors present Lightweight Ranking Heads, a way to add new tasks on the fly without retraining the main model. This method keeps new tasks separate and allows updating many models at once, making experiments much faster. It has been successfully used at YouTube, cutting experiment time from weeks to days.
Open → 2609.25433v1