The analysis of green advertisement communication strategy based on deep <scp>factorization machine</scp> deep learning model under supply chain management

Yu, Xue; Zhu, Yuemin; Jia, Congcong; Lu, Wenzhou; Xu, Hao

doi:10.1111/exsy.13258

Cited by 1 publication

(2 citation statements)

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“…Still, an empirical and prototype computational model for advertisement popularity prediction based on these machine learning features is missing in the published literature that will undoubtedly be a great support to the advertising industry. The computational models that can provide text, image and video features based on advertisement popularity prediction can provide generic and genuine support to advertising agencies of different domains such as education, healthcare (Varoquaux & Cheplygina, 2022), supply chain management (Yu et al, n.d.), travel tourism (Kontogianni et al, 2022), and so on. For any domain, enticing and engaging the customer with products is done using published media content (text, image or video) and prediction of popularity by finding an association of content with consumer engagement is the viable and only solution for advertising of any domain.…”

Section: Related Research and Supporting Evidencementioning

confidence: 99%

“…There is a need for a prediction model using handcrafted and auto‐generated deep learning advertisement features (Yu et al, n.d.) to get consumers' perception of the advertisement and media content (Christy et al, 2023; Lv et al, 2022; Zhang et al, 2022). These consumer perception insights can help the advertising industry to understand the audience preferences and ultimately the most engaging content (Hussain et al, 2022; Ullah & Anwar, 2020).…”

Section: Introductionmentioning

confidence: 99%

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A novel multimodal online news popularity prediction model based on ensemble learning

et al. 2023

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The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical measures to prove a hypothesis. However, predicting news popularity based on statistical measures applied to past data is highly questionable. Therefore, in this article, we predict news popularity using machine learning classification models and deep residual neural network models. Articles are usually made up of textual content and in many cases, images are also used. Although it is evident that the appropriate amount of textual data is required to extract features and create models, image data is also helpful in gaining useful information. In this article, we present a novel multimodal online news popularity prediction model based on ensemble learning. This research work acts as a guide for extensive feature engineering, feature extraction, feature selection and effective modelling to create a robust news popularity Prediction Model. Three kinds of features—meta‐features, text features and image features are used to design an influential and robust model. The relative error performance measure Root Mean Squared logarithmic error (RMSLE) is used to quantify the popularity prediction error. Further, the RMSLE outcome shows 0.351 which is the lowest error value given by the proposed model. Further, the most important features are also sought out to show the dependence of the best‐fit model on text and image features.

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Section: Related Research and Supporting Evidencementioning

confidence: 99%