Scalable and Lightweight On-Device Recommender Systems

可扩展且轻量级的设备推荐系统

• 批准号: DE230101033
• 负责人: Dr Tong Chen
• 金额: $ 29.61万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE230101033
• 关键词: Scalable; Lightweight; Device; Recommender; Systems

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.

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本质。该项目期望在设备上机器学习和推荐系统的交叉领域产生新知识。预期成果包括一个新颖的自动部署平台，该平台可以为每个用户设备的配置高效地定制模型，支持设备上的推荐和模型更新，并且计算量极小。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创新业务系统所需的工具，并具有安全和成本效益的优势。