Abstract
Online travel agencies (OTAs) aim to use digital media advertisements within the most efficient way for increasing 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, the 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. After that using these predictions, we aim to forecast the next day’s sales amount in order to provide an estimate of daily revenue generated per hotel. An important contribution of our study is to use an enriched dataset constructed by combining the most informative features proposed in various related studies for click and sales prediction. In this study, the data which is generated by one of the biggest OTA in Turkey and we provide a real-world application for OTA’s. The results both for Impression, CTR and sales prediction show that enrichment of the input representation with the OTA-specific additional features increases the generalization ability of the prediction models, and tree-based boosting algorithms perform the best results on this task.
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References
Adam-Bourdarios, C., Cowan, G., Germain-Renaud, C., Guyon, I., K´egl, B., Rousseau, D.: The higgs machine learning challenge. J. Phys.: Conf. Ser. 664, 1–8 (2015)
Babajide Mustapha, I., Saeed, F.: Bioactive molecule prediction using extreme gradient boosting. Molecules 21(8), 983 (2016)
Balfer, J., Bajorath, J.: Systematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysis. PLoS One 10(3), 0119301 (2015)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Chen, T., He, T., Benesty, M.: Xgboost: extreme gradient boosting. R package version 0.4-2, 1–4 (2015)
Cootes, T.F., Ionita, M.C., Lindner, C., Sauer, P.: Robust and accurate shape model fitting using random forest regression voting. In: European Conference on Computer Vision, pp. 278–291. Springer (2012)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29 1189–1232 (2001)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Hengl, T., de Jesus, J.M., Heuvelink, G.B., Gonzalez, M.R., Kilibarda, M., Blagoti´c, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B.: Soilgrids250m: global gridded soil information based on machine learning. PLoS One, 12(2), e0169748 (2017)
Malani, J., Sinha, N., Prasad, N., Lokesh, V.: Forecasting bike sharing demand. USCD-CSE (2016)
Mangal, A., Kumar, N.: Using big data to enhance the bosch production line performance: a kaggle challenge. In Big Data (Big Data), 2016 IEEE International Conference on, pp. 2029–2035 (2016)
Nabi-Abdolyousefi, R.: Conversion rate prediction in search engine marketing. PhD thesis (2015)
Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, New York, pp. 521–530 (2007)
Ridgeway, G., Madigan, D., Richardson, T.: Boosting methodology for regression problems. In: AISTATS (1999)
Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and qsar modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)
Wang, F., Suphamitmongkol, W., Wang, B.: Advertisement click-through rate prediction using multiple criteria linear programming regression model. Procedia Comput. Sci. 17, 803–811 (2013)
Zhou, Z.-H., Feng, J.: Deep forest: towards an alternative to deep neural networks (2017). arXiv preprint arXiv:1702.08835
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Tekin, A.T., Cebi, F. (2020). Click and Sales Prediction for Digital Advertisements: Real World Application for OTAs. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_26
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DOI: https://doi.org/10.1007/978-3-030-23756-1_26
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