To address privacy concerns and reduce network latency, there has been a recent trend of compressing cumbersome recommendation models trained on the cloud and deploying compact recommender models to resource-limited devices for the real-time recommendation. Existing solutions generally overlook device heterogeneity and user heterogeneity. They require devices with the same budget to share the same model and assume the available device resources (e.g., memory) are constant, which is not reflective of reality. Considering device and user heterogeneities as well as dynamic resource constraints, this paper proposes a Personalized Elastic Embedding Learning framework (PEEL) for the on-device recommendation, which generates Personalized Elastic Embeddings (PEEs) for devices with various memory budgets in a once-for-all manner, adapting to new or dynamic budgets, and addressing user preference diversity by assigning personalized embeddings for different groups of users. Specifically, it pretrains a global embedding table with collected user-item interaction instances and clusters users into groups. Then, it refines the embedding tables with local interaction instances within each group. PEEs are generated from the group-wise embedding blocks and their weights that indicate the contribution of each embedding block to the local recommendation performance. Given a memory budget, PEEL efficiently generates PEEs by selecting embedding blocks with the largest weights, making it adaptable to dynamic memory budgets on devices. Furthermore, a diversity-driven regularizer is implemented to encourage the expressiveness of embedding blocks, and a controller is utilized to optimize the weights. Extensive experiments are conducted on two public datasets, and the results show that PEEL yields superior performance on devices with heterogeneous and dynamic memory budgets.

翻译：为解决隐私问题并降低网络延迟，近年来的研究趋势是将云端训练的庞大推荐模型压缩后部署到资源受限设备上，以实现实时推荐。现有方案普遍忽略设备异构性与用户异构性，要求相同预算的设备共享同一模型，并假设可用设备资源（如内存）恒定不变，这与现实情况不符。针对设备与用户的异构性及动态资源约束，本文提出面向设备端推荐的个性化弹性嵌入学习框架PEEL，该框架能够以"一次训练、处处适用"的方式为不同内存预算的设备生成个性化弹性嵌入（PEEs），适应新增或动态变化的预算规模，并通过为不同用户群组分配个性化嵌入来应对用户偏好多样性。具体而言，该框架首先利用收集的用户-项目交互实例预训练全局嵌入表，并将用户聚类为群组；随后基于各组内的局部交互实例优化嵌入表。PEEs由群组级嵌入块及其权重生成，其中权重表示各嵌入块对局部推荐性能的贡献。在给定内存预算时，PEEL通过选择权重最大的嵌入块高效生成PEEs，从而适应设备端动态内存预算。此外，研究引入多样性驱动正则化项增强嵌入块的表达能力，并采用控制器优化权重。在两个公开数据集上的实验结果表明，PEEL在异构与动态内存预算设备上均取得卓越性能。