  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. In response to these challenges, we propose
a customizeD slImming framework for incompatiblE neTworks(DIET). DIET deploys
the same generic backbone (potentially incompatible for a specific edge) to all
devices. To minimize frequent bandwidth usage and storage consumption in
personalization, DIET tailors specific subnets for each edge based on its past
interactions, learning to generate slimming subnets(diets) within incompatible
networks for efficient transfer. It also takes the inter-layer relationships
into account, empirically reducing inference time while obtaining more suitable
diets. We further explore the repeated modules within networks and propose a
more storage-efficient framework, DIETING, which utilizes a single layer of
parameters to represent the entire network, achieving comparably excellent
performance. The experiments across four state-of-the-art datasets and two
widely used models demonstrate the superior accuracy in recommendation and
efficiency in transmission and storage of our framework.
