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

随着大数据时代的到来，推荐系统在大数据的四个维度--大容量、低准确率、高速度和高多样性--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，利用大数据实现可扩展、强大和实时的推荐。该项目旨在应对这些挑战，以实现可扩展、稳健和实时的建议。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器框架。该项目有可能大大减少大规模推荐系统的能源消耗，并有助于增加大数据推荐应用程序的使用。

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.