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：由于移动边缘计算能力的持续提升，推荐系统开始将模型部署于边缘设备以缓解频繁移动请求导致的网络拥塞。现有研究利用边缘侧接近实时数据的优势，通过微调构建边缘专用模型。尽管这些方法取得了显著进展，但其需要大量边缘计算资源和频繁的网络传输以保持模型更新。前者可能通过抢占计算资源干扰边缘其他进程，后者则消耗网络带宽，导致用户满意度下降。针对这些挑战，我们提出面向不兼容网络的定制化瘦身框架DIET。DIET将相同的通用主干网络（可能对特定边缘不兼容）部署至所有设备。为最小化个性化过程中的带宽占用与存储消耗，DIET基于各边缘的历史交互数据为其定制专属子网，学习在不兼容网络内生成高效传输的瘦身子网（即"饮食方案"）。该框架同时考虑网络层间关联性，在实证中既能减少推理时间，又能获得更适配的瘦身方案。我们进一步探索网络中的重复模块结构，提出存储效率更高的DIETING框架，该框架仅使用单层参数表征整个网络，在保持优异性能的同时显著提升存储效率。在四个前沿数据集和两种主流模型上的实验表明，本框架在推荐准确性、传输效率与存储效率方面均具有显著优势。