One Person, One Model--Learning Compound Router for Sequential Recommendation

Understand

Deep learning has brought significant breakthroughs in sequential recommendation (SR) for capturing dynamic user interests.

• A series of recent research revealed that models with more parameters usually achieve optimal performance for SR tasks, inevitably resulting in great challenges for deploying them in real systems.
• Following the simple assumption that light networks might already suffice for certain users, in this work, we propose CANet, a conceptually simple yet very scalable framework for assigning adaptive network architecture in an input-dependent manner to reduce unnecessary computation.
• The core idea of CANet is to route the input user behaviors with a light-weighted router module.