Collaborative filtering is a product recommendation technique for making automated product suggestions to a user based on the preference information from similar users. Traditional recommendation algorithms drive personalized recommendations using the data from user purchases and ratings. For e-book retailers, besides user purchases and ratings, product features such as book content and metadata also provide […]

The ultimate aim of this project is to design and develop methods and tools for classifying attributes of books such as genre, style, tone, and likelihood of being popular. Towards this end we will make use of various information types available on books and users of the Kobo catalog, including the text, meta-data associated with […]

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 […]

During the duration of the proposed project, the intern will become a member of the partner organization’s “Big Data” team, participating in all team activities, and completing research-oriented work for the team. The intern will complete research that will explore and improve upon previous methods of intelligently communicating with customers through personalized emails. This may […]

Search and recommendations drive the majority of sales at KOBO. It is therefore critical to continually improve and tune these algorithms. Our project will explore several questions dealing with integrating information provided by users in order to optimize search and recommendation algorithms: how to integrate user-generated tags in order to enhance search (rather than simply […]