CTR Prediction Using Wide & Deep and CCPM

Predicting ad click-through rates (CTR) is a multi-billion dollar industry. Advertisers use CTR to track and predict how well their advertising will work. The CTR prediction predicts whether a user will click on an advertisement based on several user factors, such as device, kind of requirement, ad placement, and so on. The number of clicks on a given ad is calculated as the number of impressions divided by the total number of views. The CTR prediction has been utilised for real-time bidding on various sorts of advertising displayed by various firms depending on user requirements and other criteria such as their preferences, searches, price filters, and so on. Two deep learning approaches, CCPM and the Wide & Deep algorithm, are used in this research study, and their comparative analysis on the Avazu dataset is explained using the LogLoss and ROC AUC graphs. It is seen as the Wide and Deep model will have excellent logloss scores and it is highly stable.