Adaptive User Interfaces for Product Recommender Systems

We are in the process of creating and growing a team of researchers, expert in the field of machine learning and data-mining. Ultimately, our aim is to create solutions to eliminate the need to manually define personalization strategies. We are in the process of signing partnership agreements with retailers capable of collecting large-scale datasets of customer behaviour. Through a data-sharing/consulting partnership we plan to perform research on the design of recommender systems customized for the data-sets available to brick and mortar retailers. These methods can be used in their physical and online loyalty programs as well as in their dynamical promotions/pricing strategies.

Faculty Supervisor:

Cristina Conati

Student:

Ehsan Mahmoudzadehvazifeh

Partner:

Qi-Leap Analytics Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of British Columbia