Demand prediction for e-commerce advertisements: A comparative study using state-of-the-art machine learning methods

Rai, Sanket; Gupta, Aditya; Anand, Adarsh; Trivedi, Aditya; Bhadauria, Saumya

doi:10.1109/icccnt45670.2019.8944783

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…To obtain forecasts of demand with different advertising expenses, five ML techniques (SVM, DTR, RFR, ANN and LSTM) have been used. ML techniques have been chosen for demand forecasting since previous studies have shown that ML techniques are superior to traditional statistical techniques in time series demand forecasting (Bajari et al, 2015;Carbonneau et al, 2008;Di Pillo et al, 2013;Rai, et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…In ML applications, the use of very large datasets may reduce performance of the prediction as many noisy variables impact on the algorithm (Nguyen, Nguyen, et al, 2020;Van Nguyen, Zhou, et al, 2020). Moreover, while there is a vast array of literature on sales and demand forecasting, it has been found that the effects of promotions, especially advertising, has been neglected (Di Pillo et al, 2013;Kumar et al, 2020;Rai et al, 2019). Besides, current studies generally examine the promotion or only the advertising effect on demand/sales using traditional methods (Divakar et al, 2005;Trapero et al, 2015;Van Donselaar et al, 2016).…”

mentioning

confidence: 99%

“…A few studies have used ML methods to estimate demand based on promotions (Ferreira et al, 2016;Abolghasemi et al, 2020;Ali & Gürlek, 2020). The number of studies using ML methods and estimating demand by focusing on just advertisements is even smaller (Bollapragada et al, 2008;Güler, 2019;Rai et al, 2019). Therefore, to the best of our knowledge, there is a gap in current literature about predicting demand based on advertising data using ML methods.…”

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confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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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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“…Most modern computers are based on a stored-program concept, i.e., the von Neumann architecture [1], where instructions and data are stored in a separate memory and handled the same. Therefore, when processing low-locality data-intensive applications, such as recently emerging DNN (Deep Neural Network) [2], e-commerce [3], [4], [5], [6] The associate editor coordinating the review of this manuscript and approving it for publication was Mario Donato Marino .…”

Section: Introductionmentioning

confidence: 99%

PISA-DMA: Processing-in-Memory Instruction Set Architecture Using DMA

et al. 2023

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Processing-in-memory (PIM) has attracted attention to overcome the memory bandwidth limitation, especially for computing memory-intensive DNN applications. Most PIM approaches use the CPU's memory requests to deliver instructions and operands to the PIM engines, making a core busy and incurring unnecessary data transfer, thus, resulting in significant offloading overhead. DMA can resolve the issue by transferring a high volume of successive data without intervening CPU and polluting the memory hierarchy, thus perfectly fitting the PIM concept. However, the small computing resources of DRAM-based PIM devices allow us to transfer only small amounts of data at one DMA transaction and require a large number of descriptors, thus still incurring significant offloading overhead. This paper introduces PIM Instruction Set Architecture (ISA) using a DMA descriptor called PISA-DMA to express a PIM opcode and operand in a single descriptor. Our ISA makes PIM programming intuitive by thinking of committing one PIM instruction as completing one DMA transaction and representing a sequence of PIM instructions using the DMA descriptor list. Also, PISA-DMA minimizes the offloading overhead while guaranteeing compatibility with commercial platforms. Our PISA-DMA eliminates the opcode offloading overhead and achieves 1.25x, 1.31x, and 1.29x speedup over the baseline PIM at the sequence length of 128 with the BERT, RoBERTa, and GPT-2 models, respectively, in ONNX runtime with real machines. Also, we study how our proposed PISA affects performance in compiler optimization and show that the operator fusion of matrix-matrix multiplication and element-wise addition achieved 1.04x speedup, a similar performance gain using conventional ISAs.

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Sales Prediction in E-Commerce Platforms Using Machine Learning

Aljbour,

Avcı

2024

Lecture Notes in Networks and Systems

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Demand prediction for e-commerce advertisements: A comparative study using state-of-the-art machine learning methods

Cited by 12 publications

References 15 publications

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

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

PISA-DMA: Processing-in-Memory Instruction Set Architecture Using DMA

Sales Prediction in E-Commerce Platforms Using Machine Learning

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