Online ad effectiveness evaluation with a two-stage method using a Gaussian filter and decision tree approach

Wang, Heyong; Hong, Minghui

doi:10.1016/j.elerap.2019.100852

Cited by 17 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the volume of resources spent daily on online advertising it is essential for all stakeholders to be able to measure the performance and effectiveness of ads. Advertisers invest money to reach out potential customers in order to increase sales and profits, and thus they need to evaluate their return on the investment [16]. This is a challenging task since user perception of ads can be connected to numerous factors such as browsing behaviour (if the user is surfing the Internet aimlessly or not) [17] or the content of the webpage itself [18], among many others.…”

Section: State Of the Artmentioning

confidence: 99%

Analyzing and Testing Viewability Methods in an Advertising Network

2020

View full text Add to dashboard Cite

show abstract

Section: State Of the Artmentioning

confidence: 99%

Analyzing and Testing Viewability Methods in an Advertising Network

2020

View full text Add to dashboard Cite

show abstract

“…This technique has been used to predict the value of reviews [69], [82], the choice of a brand based on social networks [81], and sales [83], [84]. Notably, it is one of the most used supervised techniques to be included in recommender systems [63], [85]- [88].…”

Section: ) Decision Tree (Dt)mentioning

confidence: 99%

“…In the same way, a study focused on customers, studying their loyalty and asset management in hotels [85], the inconsistencies in their opinions in a cognitive purchase decision-making process [53], the classification of their elements directly for predictive recommendation [39], and their experiences using chatbots [23]. Publicity and campaigns have also used ML techniques to combine means of publicity (television and online) [72], estimate when, what, and how much to spend on publicity to increase profits [142], efficiently evaluate online publicity [88], and optimize micro-focalized techniques of campaigns [143]. Other applications of recommender systems are found in brand management, associated with personality, identification of associations and potential collaborations [144], or in the investigation of the moderating effects of consumer knowledge (expertise) in a beer recommendation [145].…”

Section: B Recommender Systemsmentioning

confidence: 99%

Machine Learning and Marketing: A Systematic Literature Review

2022

View full text Add to dashboard Cite

Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008-2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis-content analysis.

show abstract

“…The criterion used for the extra trees classifier was "gini". The criterion used for the decision tree classifier was also "gini" [48]. The specific results are shown in [49].…”

Section: Comparison Experiments With Machine Learning Classifiersmentioning

confidence: 99%

Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline riser using a Doppler ultrasonic sensor and deep neural networks

Nnabuife

Kuang

Whidborne

et al. 2021

Chemical Engineering Journal

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Online ad effectiveness evaluation with a two-stage method using a Gaussian filter and decision tree approach

Cited by 17 publications

References 38 publications

Analyzing and Testing Viewability Methods in an Advertising Network

Analyzing and Testing Viewability Methods in an Advertising Network

Machine Learning and Marketing: A Systematic Literature Review

Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline riser using a Doppler ultrasonic sensor and deep neural networks

Contact Info

Product

Resources

About