Challenging big data for scalable, robust and real-time recommendations

挑战大数据以提供可扩展、稳健和实时的建议

• 批准号: DP190101985
• 负责人: Prof Hongzhi Yin
• 金额: $ 24.57万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2019
• 资助国家: 澳大利亚
• 起止时间: 2019-03-08 至 2023-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP190101985
• 关键词: Challenging; big; data; scalable; robust

With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-time recommendations. This project will devise a series of cost-effective machine learning methods and schemes to deliver an end-to-end recommender framework. This project has the potential to significantly reduce the energy consumption of large-scale recommender systems as well as facilitating an increase in the use of recommendation applications for big data.

随着大数据时代的到来，推荐系统在大数据的四个维度：海量、低准确性、高速度和高多样性上面临着前所未有的挑战。该项目旨在开发新一代具有成本效益的技术，用于利用大数据进行可扩展，强大和实时的建议。该项目旨在解决这些挑战，以实现可扩展，强大和实时的建议。该项目将设计一系列具有成本效益的机器学习方法和方案，以提供端到端的推荐框架。该项目有可能显着降低大规模推荐系统的能耗，并促进大数据推荐应用程序的使用增加。