A system for explainable recommendations

This research aims to develop a system that generates explainable recommendations. Mobio

currently allows merchants to offer items to users, but does not employ recent advances in

recommender systems. This project will allow Mobio to build on the expertise of Dr. Chiang in

related relational learning problems to create such a system, and provide a real-world domain

for him to advance the state-of-the-art. A core problem in recommender systems is building

models of user preference that are predictive and explainable. We propose to apply and

evaluate algorithms developed by Dr. Chiang to build models that are optimized for accuracy,

explainable to users, and recommend offers from merchants to the most appropriate users.

The expected outcomes of this work include (i) enhanced user engagement through

explainable recommendations, and (ii) improved effectiveness of offers by participating

merchants. Addressing the interests of both users and merchants directly increases the

commercial value of Mobio’s products.

Faculty Supervisor:

David Poole

Student:

Partner:

Mobio Technologies Inc

Discipline:

Computer science

Sector:

University:

The University of British Columbia

Program:

Accelerate

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