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.

翻译：由于移动边缘设备能力的持续提升，推荐系统开始将模型部署在边缘侧，以缓解频繁移动请求造成的网络拥塞。已有研究利用边缘侧靠近实时数据的优势，通过微调创建边缘专属模型。尽管这些方法取得了显著进展，但它们需要消耗大量边缘计算资源，并通过频繁的网络传输来保持模型更新。前者可能因争夺计算资源而干扰边缘设备上的其他进程，后者则消耗网络带宽，导致用户满意度下降。针对这些挑战，我们提出了一种面向不兼容网络的定制化精简框架DIET。DIET将相同的通用骨干网络（可能不适用于特定边缘设备）部署到所有设备上。为最小化个性化过程中的频繁带宽占用和存储消耗，DIET基于每个边缘设备的历史交互为其定制特定子网络，学习在不兼容网络中生成精简子网络（即"饮食方案"）以实现高效传输。该方法还考虑了层间关联性，在获得更合适的精简子网络的同时，经验性地减少了推理时间。我们进一步探索了网络中的重复模块，提出了更具存储效率的框架DIETING，利用单层参数表示整个网络，实现了同样卓越的性能。在四个最先进数据集和两个广泛使用模型上的实验表明，我们的框架在推荐准确性、传输效率和存储效率方面均表现出优越性。