Principal Component Analysis Of TF-IDF In Click Through Rate Prediction

Ankita Pal

This paper presents a model to predict the probability whether a user will click on a particular advertisement or not.  The dataset used is that of Avito.ru provided as a part of the Kaggle competition- “Avito Contextual Ads Prediction”. Here Principal Component Analysis on the Search and Query features is used, some extra count variables are made for integrate the categorical variables. Lastly logistic regression, SVM and gradient boosting algorithms are applied for classification into click or no-clicks.