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Recommendation systems have been extensively studied by many literature inthe past and are ubiquitous in online advertisement, shoppingindustry/e-commerce, query suggestions in search engines, and friendrecommendation in social networks. Moreover,restaurant/music/product/movie/news/app recommendations are only a few of theapplications of a recommender system. A small percent improvement on the CTRprediction accuracy has been mentioned to add millions of dollars of revenue tothe advertisement industry. Click-Through-Rate (CTR) prediction is a specialversion of recommender system in which the goal is predicting whether or not auser is going to click on a recommended item. A content-based recommendationapproach takes into account the past history of the user's behavior, i.e. therecommended products and the users reaction to them. So, a personalized modelthat recommends the right item to the right user at the right time is the keyto building such a model. On the other hand, the so-called collaborativefiltering approach incorporates the click history of the users who are verysimilar to a particular user, thereby helping the recommender to come up with amore confident prediction for that particular user by leveraging the widerknowledge of users who share their taste in a connected network of users. Inthis project, we are interested in building a CTR predictor using Graph NeuralNetworks complemented by an online learning algorithm that models such dynamicinteractions. By framing the problem as a binary classification task, we haveevaluated this system both on the offline models (GNN, Deep FactorizationMachines) with test-AUC of 0.7417 and on the online learning model withtest-AUC of 0.7585 using a sub-sampled version of Criteo public datasetconsisting of 10,000 data points.

Click-Through Rate Prediction Using Graph Neural Networks and Online Learning