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An Engineering Analytics Based Framework for Computational Advertising Systems

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Date Issued:
2018
Abstract/Description:
Engineering analytics is a multifaceted landscape with a diversity of analytics tools which comes from emerging fields such as big data, machine learning, and traditional operations research. Industrial engineering is capable to optimize complex process and systems using engineering analytics elements and the traditional components such as total quality management. This dissertation has proven that industrial engineering using engineering analytics can optimize the emerging area of Computational Advertising. The key was to know the different fields very well and do the right selection. However, people first need to understand and be experts in the flow of the complex application of Computational Advertising and based on the characteristics of each step map the right field of Engineering analytics and traditional Industrial Engineering. Then build the apparatus and apply it to the respective problem in question.This dissertation consists of four research papers addressing the development of a framework to tame the complexity of computational advertising and improve its usage efficiency from an advertiser's viewpoint. This new framework and its respective systems architecture combine the use of support vector machines, Recurrent Neural Networks, Deep Learning Neural Networks, traditional neural networks, Game Theory/Auction Theory with Generative adversarial networks, and Web Engineering to optimize the computational advertising bidding process and achieve a higher rate of return. The system is validated with an actual case study with commercial providers such as Google AdWords and an advertiser's budget of several million dollars.
Title: An Engineering Analytics Based Framework for Computational Advertising Systems.
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Name(s): Chen, Mengmeng, Author
Rabelo, Luis, Committee Chair
Lee, Gene, Committee Member
Keathley, Heather, Committee Member
Rahal, Ahmad, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2018
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Engineering analytics is a multifaceted landscape with a diversity of analytics tools which comes from emerging fields such as big data, machine learning, and traditional operations research. Industrial engineering is capable to optimize complex process and systems using engineering analytics elements and the traditional components such as total quality management. This dissertation has proven that industrial engineering using engineering analytics can optimize the emerging area of Computational Advertising. The key was to know the different fields very well and do the right selection. However, people first need to understand and be experts in the flow of the complex application of Computational Advertising and based on the characteristics of each step map the right field of Engineering analytics and traditional Industrial Engineering. Then build the apparatus and apply it to the respective problem in question.This dissertation consists of four research papers addressing the development of a framework to tame the complexity of computational advertising and improve its usage efficiency from an advertiser's viewpoint. This new framework and its respective systems architecture combine the use of support vector machines, Recurrent Neural Networks, Deep Learning Neural Networks, traditional neural networks, Game Theory/Auction Theory with Generative adversarial networks, and Web Engineering to optimize the computational advertising bidding process and achieve a higher rate of return. The system is validated with an actual case study with commercial providers such as Google AdWords and an advertiser's budget of several million dollars.
Identifier: CFE0007319 (IID), ucf:52118 (fedora)
Note(s): 2018-12-01
Ph.D.
Engineering and Computer Science, Industrial Engineering and Management Systems
Doctoral
This record was generated from author submitted information.
Subject(s): Engineering Analytic -- Computational Advertising -- Machine Learning -- Deep Learning -- Total Quality Management -- Auction Theory -- Web Engineering
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007319
Restrictions on Access: campus 2021-12-15
Host Institution: UCF

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