HINCare: A Heterogeneous Information Network for Elderly-Care Helper Recommendation
Grant Data
Project Title
HINCare: A Heterogeneous Information Network for Elderly-Care Helper Recommendation
Principal Investigator
Professor Cheng, Chun Kong Reynold
(Principal Investigator (PI))
Co-Investigator(s)
Dr Lau Vincent
(Co-Investigator)
Professor Luo Hao
(Co-Investigator)
Professor Lum Terry Yat Sang
(Co-Investigator)
Mr Ng Shan Hoi
(Co-Investigator)
Professor Kao Chi Ming
(Co-Investigator)
Mr Yau Yat Fan
(Co-Investigator)
Duration
12
Start Date
2021-03-01
Completion Date
2022-02-28
Amount
226800
Conference Title
HINCare: A Heterogeneous Information Network for Elderly-Care Helper Recommendation
Keywords
Elderly-Care Helper, Heterogeneous Information Network, HINCare, Recommendation
Discipline
Others - Computing Science and Information Technology
Panel
Engineering (E)
HKU Project Code
InP/420/20
Grant Type
Research Talent Hub for ITF Projects (RTH-ITF)
Funding Year
2020
Status
Completed
Objectives
Hong Kong is facing an increase in aging population: one third of the population, or 2.37 million citizens, is estimated to be aged 65 or above in 2037. About 13% of them live alone, and 24% live with their spouses only. This places a huge demand on helpers (e.g., domestic helpers and care workers) for performing tasks such as meal delivery, escort services, and household duties. Currently, there is a serious shortage of manpower for those services. We aim to develop HINCare, a software platform that suggests helpers from social networks to elderly-care organizations. To achieve this goal, we will construct a gigantic heterogeneous information network (HIN), which originates from Big Data sources, such as social networks and senior citizens' profiles. Inspired by our two recently funded RGC GRF projects, we plan to deliver effective, efficient, and scalable AI-based solutions that seek potential helpers for elderly people. We plan to use HINCare to recommend potential full-time/part-time/voluntary helpers to two local NGOs. Our project outcome will inspire AI-enabled applications on ""Big elderly-care Data"", and will also allow elderly-care organizations to adapt state-of-the-art IT solutions. The HIN database created by the project will be valuable for elderly-care-related research.