Recommendation engine, based on scalable, interpretable Bayesian Opposition based classifier.
Blind recommendation engine is a ML service designed to filter out materials for blind and partially blind students. It is built on the principles of transparency, interpretability and efficiency of learning. For its operation it requires no previous personal information about the users; through students’ interactions with the system, it learns their preferences which it then displays to the teachers.
Recommendation engine is built upon ISO accessibility guidelines. With inception of this service, our core assumption was that different standards are differently important to different students. A single material (that conforms to some but not all standards) can thus be accessible to one student but inaccessible to another. This basic assumption was confirmed in our beta testing and presented in the conference (Molan, Bulathwela, Orlič 2020).