Prediction Demand for Classified Ads Using Machine Learning

Adnane, Marouane; El, Mohammed; Fkihi, Sanaa El; Thami, Rachid Oulad Haj

doi:10.1145/3320326.3320371

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…Therefore, as Bodenstab (2017) and Ali et al (2009) have stated, ML is suitable for accurate forecasting demand arising from advertising and promotional activity. On the other hand, only a few researchers have used ML techniques by focusing on demand forecasting and advertising (Adnane et al, 2019;Bollapragada et al, 2008;Di Pillo et al, 2013;Güler, 2019;Khakpour, 2020;Rai et al, 2019;Shilpi and Sharma, 2016;Tugay and Oguducu, 2017). Furthermore, Rai et al (2019) have revealed that advertisements are effective in determining product demand by using Artificial Neural Networks, a tool of ML.…”

Section: Literature Reviewmentioning

confidence: 99%

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

et al. 2022

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In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory B Yigit Kazancoglu

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