Personalised Recommender Systems for Health and Wellbeing

Overview

This project explores how artificial intelligence can provide personalised recommendations that support health, wellbeing, and behaviour change.

Unlike many existing recommender systems, which primarily rely on historical interactions or chronological time, I am interested in whether recommendations can be improved by incorporating wearable-derived physiological and behavioural state.

Research Vision

My long-term research aims to develop explainable recommender systems that combine:

• wearable sensing;
• cycle-aware AI;
• personalised health modelling;
• explainable artificial intelligence.

The goal is to recommend the right intervention to the right person at the right time while providing transparent explanations that support trust and informed decision-making.

Current Status

This is an emerging research direction that builds on my current work in wearable data analysis and cycle-aware explainable AI. Current activities include reviewing the recommender systems literature, reproducing recent methods, and investigating how these approaches could be adapted for personalised health and wellbeing applications.