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%.
翻译:在线旅行社(OTA)通过元搜索投标引擎宣传其网站的出价。预测酒店收到的特定标价的点击次数是管理OTA在元搜索引擎上的广告活动的一个重要步骤,因为点击的投标次数决定了要产生的成本。在这项工作中,将各种递减器组合在一起,以改进点击预测性能。在预处理程序之后,根据样品的登机日期,将特征集分为火车和测试组。然后,通过使用XGBoost,通过显著降低特征的尺寸,将一个旅馆收到点击次数的预测作为取消特征的问题。然后,通过将Bayesian超参数优化到XGBoost、LightGBM和SGD模型,发现最佳的超参数。不同的经过培训的模式将分别测试,并组合成组合成组合模型。提出了四种备选的混合解决方案。同一测试集用于测试个人和组合模型,46个模型组合的结果显示,通过模型堆叠组合将产生10 %的预期性能。