Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines

TL;DR

This paper presents a method to improve click prediction for hotel advertisements by combining ensemble learning with Bayesian hyperparameter optimization, resulting in a 10% performance boost.

Contribution

It introduces a novel ensemble pipeline optimized with Bayesian hyperparameter tuning for click prediction in online travel agency advertising.

Findings

Ensemble models outperform individual regressors.

Bayesian hyperparameter optimization enhances model performance.

Achieved approximately 10% improvement in R2 score.

Abstract

Online travel agencies (OTA's) advertise their website offers on meta-search bidding engines. The problem of predicting the number of clicks a hotel would receive for a given bid amount is an important step in the management of an OTA's advertisement campaign on a meta-search engine, because bid times number of clicks defines the cost to be generated. Various regressors are ensembled in this work to improve click prediction performance. Following the preprocessing procedures, the feature set is divided into train and test groups depending on the logging date of the samples. The data collection is then subjected to feature elimination via utilizing XGBoost, which significantly reduces the dimension of features. The optimum hyper-parameters are then found by applying Bayesian hyperparameter optimization to XGBoost, LightGBM, and SGD models. The different trained models are tested separately as well as combined to form ensemble models. Four alternative ensemble solutions have been suggested. The same test set is used to test both individual and ensemble models, and the results of 46 model combinations demonstrate that stack ensemble models yield the desired R2 score of all. In conclusion, the ensemble model improves the prediction performance by about 10%.