ML-based link prediction algorithm for RWA

Artificial Intelligence (AI) and Machine Learning (ML) are key technologies in the development of a new recommendation framework that is able to identify the association between users and items with greater accuracy. This link prediction algorithm can be used in economics, marketing, networking, and social media. It would decrease the rate of mispredictions and would allow businesses to target their customers properly and increase their profit margin. The aim of this project is to develop a ML-based link prediction algorithm for the Refresh Wellness App (RWA) to discover the associations in its mental health resources system and provide the best resource recommendations for users. The performance of the recommender system will be evaluated in terms of accuracy. Moreover, the reliability of the developed model will be validated with case studies.

Faculty Supervisor:

Raymond Spiteri

Student:

Partner:

Refresh Enterprises Inc.

Discipline:

Computer science

Sector:

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

University:

University of Saskatchewan

Program: