Scalable and Lightweight On-Device Recommender Systems

该项目旨在解决现有基于云的个性化推荐服务的资源密集型和非弹性特性。该项目希望在设备上机器学习和推荐系统的交叉领域产生新的知识。预期的结果包括一个新颖的自动部署平台，可以有效地为每个用户设备的配置定制模型，支持设备上的推荐和模型更新，并且占用很小的计算空间。这些成果的好处将使澳大利亚处于人工智能的前沿，并为众多企业提供部署具有安全和成本效益优势的创新业务系统所需的工具。

This project aims to address the resource-intensive and non-resilient nature of existing cloud-based personalised recommendation services. This project expects to generate new knowledge in the intersection of on-device machine learning and recommender systems. The expected outcomes include a novel auto-deployment platform that can efficiently customise a model for each user device's configuration, supporting on-device recommendation and model updates with tiny computational footprints. The benefits of these outcomes will position Australia at the forefront of AI and give numerous businesses the tools needed to deploy innovative business systems with a secure and cost-effective advantage.