Accurate Prediction of Advertisement Clicks based on Impression and Click-Through Rate using Extreme Gradient Boosting

摘要

Online travel agencies (OTAs) aim to use digital media advertisements in the most efficient way to increase their market share. One of the most commonly used digital media environments by OTAs are the metasearch bidding engines. In metasearch bidding engines, many OTAs offer daily bids per click for each hotel to get reservations. Therefore, management of bidding strategies is crucial to minimize the cost and maximize the revenue for OTAs. In this paper, we aim to predict both the impression count and Click-Through-Rate (CTR) metrics of hotel advertisements for an OTA and then use these values to obtain the number of clicks the OTA will take for each hotel. The initial version of the dataset was obtained from the dashboard of an OTA which contains features for each hotel's last day performance values in the search engine. We enriched the initial dataset by creating features using window-sliding approach and integrating some domain-specific features that are considered to be important in hotel click prediction. The final set of features are used to predict next day's CTR and impression count values. We have used state-of-the-art prediction algorithms including decision tree-based ensemble methods, boosting algorithms and support vector regression. An important contribution of this study is the use of Extreme Gradient Boosting (XGBoost) algorithm for hotel click prediction, which overwhelmed state-of-the-art algorithms on various tasks. The results showed that XGBoost gives the highest R-Squared values in the prediction of all metrics used in our study. We have also applied a mutual information filter feature ranking method called minimum redundancy-maximum relevance (mRMR) to evaluate the importance of the features used for prediction. The bid value offered by OTA at time t - 1 is found to be the most informative feature both for impression count and CTR prediction. We have also observed that a subset of features selected by mRMR achieves comparable performance with using all of the features in the machine learning model.