Rich Recommendations

Item-to-item and user-to-item recommendations are prevalent on most ecommerce websites and digital content related mobile applications. At Kobo, we strive to constantly improve our recommendation system, which is based on co-purchase patterns on Kobo’s website or through Kobo eReaders and mobile apps. This internship is to explore improving the system along several dimensions: incorporating additional data sources, such as users’ ratings and reviews, and books that users have sideloaded onto devices (such as ePubs obtained from non-Kobo platforms); improving the core algorithm by introducing richer representations using deep learning; building recommendations for non-book, high-level concepts such as authors and series; building fine-grained recommendations based on subsets of the catalog to support personalization of item lists. All these explorations will be tested using A/B testing methodology, and expected results are determination of whether such ideas improve KPI metrics and full productization of the projects that lead to improvements in these KPIs.

Faculty Supervisor:

Brendan Frey


Hanyang Li


Kobo Inc.


Computer science


Media and communications


University of Toronto



Current openings

Find the perfect opportunity to put your academic skills and knowledge into practice!

Find Projects