Enhanced recommendation systems using machine learning

Artificial Intelligence (AI) and Machine Learning (ML) will play key roles in the RWA platform. A significant area to which these technologies will be applied is the recommendation algorithm connecting users to resources that are most relevant to their 3 personal wellness journey. The app features a collection of both community resources and in-app resources that have been developed by Refresh. Each of them meets different mental health needs, and not everyone will respond the same way to each resource. Therefore, Refresh is seeking a ML-based solution that will take in various user inputs–such as demographics, journal entry sentiments, and usage patterns–and output recommendations of resources that are highly suited to the user’s needs at that particular time. In this project, four interns will work together to develop and implement this model, beginning with just two features: journal entries and usage patterns. As this will represent the first ML component of the RWA platform, the implementation must be done in such a way so as to support additional ML applications in the future.

Faculty Supervisor:

Orland Hoeber;Nuelle Novik

Student:

Partner:

Refresh Enterprises Inc.

Discipline:

Computer science

Sector:

Artificial Intelligence; Health and Related Sciences & Technology; New and Digital Media

University:

University of Regina

Program: