Identifying machine learning techniques for classification of target advertising

Choi, Jina; Lim, Kiho

doi:10.1016/j.icte.2020.04.012

Cited by 84 publications

(33 citation statements)

References 18 publications

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“…ML was explored as a separate discipline in the 1990s [17], despite the fact that the earliest notions of ML (with different terminologies) were developed in the 1950s. Apart from computer science, ML algorithms are being applied in a variety of fields, including business [18], advertising [19], and medicine [20].…”

Section: B Machine Leanringmentioning

confidence: 99%

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

et al. 2022

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Machine learning algorithms have been used for the detection (and possibly) prediction of Alzheimer's disease using genotype information, with the potential to enhance the outcome prediction. However, detailed research about the analysis and the detection of Alzheimer's disease using genetic data is still in its primitive stage. The aim of this paper is to examine the scientific literature on the use of various machine learning approaches for the prediction of Alzheimer's disease based solely on genetic data. To identify gaps in the literature, critically appraise the reporting and methods of the algorithms, and provide the foundation for a wider research programme focused on developing novel machine learning based predictive algorithms in Alzheimer's disease. In our study between January 1, 2010, until September 21, 2021, we have reviewed different articles within PubMed, Web of Science, and Scopus to research into keywords and phrases linked to Alzheimer's disease and machine learning tools, including Artificial Neural Networks, boosting, and random forests. Articles were reviewed for inclusion, then retrieved, and assessed for risk of bias using Preferred Reporting Items for Systematic Reviews and Meta-analyses criteria. A pool of 150 abstracts, 65 full texts was evaluated and 24 studies were considered in the review. Machine learning methods in the reviewed papers performed in a wide range of ways (0.59 to 0.98 AUC). Our study indicated that high risk of bias in the analysis can be linked to feature selection, hyperparameter search and validation methods.

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Section: B Machine Leanringmentioning

confidence: 99%

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

et al. 2022

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“…Click fraud can damage an advertiser's return on investment significantly. It was found that 30% of ad revenue is wasted on click frauds (Choi, Lim, 2020).…”

Section: Advertising With Machine Learningmentioning

confidence: 99%

“…Behavioral targeting is used to select the most relevant advertisements for consumers and is based on historical user behavior, such as identifying clicked links, pages visited, searches, earlier purchases from the users browsing history (Choi, Lim, 2020). With the popularity of search engines, such as Google, online searches and web browsing have become two of the most common online behaviours.…”

Section: Advertising With Machine Learningmentioning

confidence: 99%

ECMS 2021 Proceedings edited by Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela

2021

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This research paper discusses the application of multiagent simulation model of production processes by reference to milk receiving and storage. The paper describes basic parameters for general models, and presents the experiment results obtained during the model processing, as well as the approach towards implementation of the multiagent system employing AnyLogic simulation environment. The introduction of general technique to plot multiagent simulation models allows to use digital tools to create a virtual copy of the true-life processes with the possibility to provide forecast and identification of the food production industries. The employment of simulation modeling enables refinement of the production process under study, identification of its weaknesses, and provision of the expert opinion on improvement of production processes and as regards the results of virtual testing thereof.

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“…Today, new artificial intelligence technologies and recommender systems are used to identify potential consumers [ 6 ]. Using the content-based approach, Mooney and Roy [ 15 ] examines the degree of similarity between consumers and product content profiles, examining parsable information, such as textual data.…”

Section: Related Workmentioning

confidence: 99%

“…Personalized advertising is a form of paid message that is “tailored to an individual’s characteristics and/or interests or tastes” [ 5 ]. Information on consumers is gathered and stored through web-based behavior-tracking and database technology, such as tracking users’ web browsing behavior, use of cookies, implementation of opt-in and opt-out options on websites, and requiring an agreement to personal data collection as a condition to use SNS platforms [ 6 ]. Marketers then use consumers’ information to strategically devise advertisements.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

Hong

Choi

Lim

et al. 2020

Sensors

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The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations.

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Identifying machine learning techniques for classification of target advertising

Cited by 84 publications

References 18 publications

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

ECMS 2021 Proceedings edited by Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

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