A recommender system for breast cancer patients

Abstract

An ongoing challenge in the information age is finding information relevant to a particular need. One area in which this is particularly problematic is the medical do- main, where patients suffering from certain conditions seek advice on managing their health. Personalized recommendations can be useful in this context. A recommender system can assist users to locate relevant information and choose the best option that matches their needs. This thesis developed a Breast Cancer Recommender System (BCRS) which rec- ommends health related articles appropriate for patients confronting breast cancer. BCRS applies a hybrid algorithm which combines collaborative filtering and content based approaches to generate recommendations. Article recommendations can be categorized in four main groups: life style, emotional concerns, risk factors and treat- ment. To examine the quality and perceived usefulness of article recommendations, a preliminary evaluation was conducted using female medical students of Memorial University.