Multi task learning improves recommendation accuracy and satisfaction
Learned Cross-Task Relationships in Multi-Task Models
Artificial Intelligence
Summary
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.
What this means in practice
- •For recommendation engineers: Improve recommendation accuracy and user satisfaction by modeling task relationships through pairwise learning within multi task models.
- •For advertising platform teams: Enhance ad targeting algorithms by better sharing information across multiple prediction tasks without modeling full complex task interactions.
Authors
Victor Zhang, Yiping Yuan, Florian Raudies, Bosun Adeoti, Brian Y. C. Leung, Sanjay Surendranath Girija, Naijing Zhang
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.