On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy. To stay current with evolving user interests, cloud-based recommender systems are periodically updated with new interaction data. However, on-device models struggle to retrain themselves because of limited onboard computing resources. As a solution, we consider the scenario where the model retraining occurs on the server side and then the updated parameters are transferred to edge devices via network communication. While this eliminates the need for local retraining, it incurs a regular transfer of parameters that significantly taxes network bandwidth. To mitigate this issue, we develop an efficient approach based on compositional codes to compress the model update. This approach ensures the on-device model is updated flexibly with minimal additional parameters whilst utilizing previous knowledge. The extensive experiments conducted on multiple session-based recommendation models with distinctive architectures demonstrate that the on-device model can achieve comparable accuracy to the retrained server-side counterpart through transferring an update 60x smaller in size. The codes are available at \url{https://github.com/xiaxin1998/ODUpdate}.

翻译：设备端推荐系统近年因能提供快速响应并保障隐私而日益受到关注。为动态适应用户兴趣演变，基于云的推荐系统需定期利用新交互数据更新。然而，设备端模型受限于本地计算资源，难以自主完成重训练。为此，我们考虑一种方案：在服务端执行模型重训练，随后将更新后的参数通过网络通信传输至边缘设备。该方法虽免去了本地重训练的需求，但需定期传输参数，显著加重了网络带宽负担。为解决这一问题，我们提出一种基于组合编码的高效模型更新压缩方法。该方法能以极少的额外参数灵活更新设备端模型，同时充分利用先前知识。在多个具有不同架构的会话推荐模型上开展的广泛实验表明，通过传输体积缩小60倍的更新参数，设备端模型可达到与重训练后服务端模型相当的准确率。相关代码已开源至 \url{https://github.com/xiaxin1998/ODUpdate}。