Duet: A Collaborative User Driven Recommendation System for Edge Devices

Recommendation systems are the backbone for numerous user applications on edge devices. However, the compute and memory-intensive nature of recommendation models renders them unsuitable for edge devices. Nevertheless, by decoupling the model fraction related to user history (e.g., past visited pages, liked posts) and user attributes (such as age, gender), we can offload partial recommendation models onto local edge devices. Hence, we present Duet, a novel collaborative edge-cloud recommendation system that intelligently decomposes the recommendation model into two smaller models - user and item models - that execute simultaneously on the edge device and cloud before coming together to deliver final recommendations. Further, we propose a lightweight Duet architecture to support user models on resource-constrained edge devices. Duet reduces the average latency by 6.4X and improves energy efficiency by 4.6X across five recommendation models.