DIET：面向序列推荐中不兼容网络的定制化瘦身
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation
AI总结：
针对边缘序列推荐中模型更新耗带宽、占资源的问题，提出 DIET 按边缘交互从不兼容骨干网络定制瘦身子网，并以 DIETING 进一步降低存储。
摘要：
由于移动边缘能力的不断提升，推荐系统开始将模型部署在边缘，以缓解频繁移动请求造成的网络拥塞。已有若干研究利用边缘侧靠近实时数据的优势，对模型进行微调以创建边缘专用模型。尽管取得了显著进展，这些方法仍需要大量边缘计算资源和频繁网络传输来保持模型更新。前者可能为获取计算资源而干扰边缘上的其他进程，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瘦身框架 DIET。DIET 将同一个通用骨干网络（可能与特定边缘不兼容）部署到所有设备。为在个性化过程中尽量减少频繁的带宽使用和存储消耗，DIET 根据每个边缘的历史交互为其定制特定子网，学习在不兼容网络中生成瘦身子网（diets），以实现高效传输。它还考虑层间关系，在实验上减少推理时间，同时获得更合适的 diets。我们进一步探索网络中的重复模块，提出存储效率更高的框架 DIETING，该框架利用单层参数表示整个网络，并取得相当优异的性能。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以及传输和存储效率方面均表现优异。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
