DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation

Understand

Due to the continuously improving capabilities of mobile edges, recommender systems start to deploy models on edges to alleviate network congestion caused by frequent mobile requests.

• Several studies have leveraged the proximity of edge-side to real-time data, fine-tuning them to create edge-specific models.
• Despite their significant progress, these methods require substantial on-edge computational resources and frequent network transfers to keep the model up to date.
• The former may disrupt other processes on the edge to acquire computational resources, while the latter consumes network bandwidth, leading to a decrease in user satisfaction.