Personalized relevance content recommendation

PC Optimum was originally built as a rewards platform. As we are moving in the path to engage more meaningfully with our customers by way of personalized & interactive experiences (content), we are building a targeting engine as a solution to surface personalized content tailored for each individual customer. The problem we are solving then becomes, given our best knowledge of the user, what is the most relevant content we can push to the user’s inspirational feed? In the project, we tackle the problem by improving product embedding and CTR prediction models.

Faculty Supervisor:

Scott Sanner

Student:

Partner:

Loblaws Digital

Discipline:

Computer science

Sector:

Retail trade

University:

University of Toronto

Program:

Accelerate

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