Real-time Bidding Using Contextual Targeting

Ads keep the internet free. But, to keep them from becoming spam and degrading the user’s online experience, they need to be relevant to them. The traditional way the industry does this is by collecting a lot of information about every user and creating profiles that can be used to target users based on their online journey. However, users have different tolerances for how much information they want collected about them by any website/app. In general, the problem the industry needs to solve is to make sure the ads shown to users are relevant even in the absence of any user profile. This is the problem the intern is going to tackle. In this research, an End-to-End project will be built to create a contextual targeting proof of concept for mobile and desktop websites. Industry standard metrics such as click-through rate will be used to get an understanding of whether contextual targeting works.

Faculty Supervisor:

Annie Lee

Student:

Partner:

Pelmorex

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program: