  Recommendation systems have been extensively studied by many literature in
the past and are ubiquitous in online advertisement, shopping
industry/e-commerce, query suggestions in search engines, and friend
recommendation in social networks. Moreover,
restaurant/music/product/movie/news/app recommendations are only a few of the
applications of a recommender system. A small percent improvement on the CTR
prediction accuracy has been mentioned to add millions of dollars of revenue to
the advertisement industry. Click-Through-Rate (CTR) prediction is a special
version of recommender system in which the goal is predicting whether or not a
user is going to click on a recommended item. A content-based recommendation
approach takes into account the past history of the user's behavior, i.e. the
recommended products and the users reaction to them. So, a personalized model
that recommends the right item to the right user at the right time is the key
to building such a model. On the other hand, the so-called collaborative
filtering approach incorporates the click history of the users who are very
similar to a particular user, thereby helping the recommender to come up with a
more confident prediction for that particular user by leveraging the wider
knowledge of users who share their taste in a connected network of users. In
this project, we are interested in building a CTR predictor using Graph Neural
Networks complemented by an online learning algorithm that models such dynamic
interactions. By framing the problem as a binary classification task, we have
evaluated this system both on the offline models (GNN, Deep Factorization
Machines) with test-AUC of 0.7417 and on the online learning model with
test-AUC of 0.7585 using a sub-sampled version of Criteo public dataset
consisting of 10,000 data points.
