دانلود مقاله ISI انگلیسی شماره 84833
ترجمه فارسی عنوان مقاله

یک روش جدید برای بهینه سازی کمپین های تبلیغاتی با استفاده از الگوریتم های ژنتیک

عنوان انگلیسی
A novel methodology for optimizing display advertising campaigns using genetic algorithms
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
84833 2018 13 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Electronic Commerce Research and Applications, Volume 27, January–February 2018, Pages 39-51

ترجمه کلمات کلیدی
نمایش کمپین های تبلیغاتی پاسخ مستقیم، بهینه سازی، الگوریتم ژنتیک، میکرو هدفگیری، فراگیری ماشین،
کلمات کلیدی انگلیسی
Display advertising campaigns; Direct response; Optimization; Genetic algorithms; Micro-targeting; Machine learning;
پیش نمایش مقاله
پیش نمایش مقاله  یک روش جدید برای بهینه سازی کمپین های تبلیغاتی با استفاده از الگوریتم های ژنتیک

چکیده انگلیسی

Online advertising campaigns have attracted the attention of many advertisers willing to promote their business on the Internet. One of the main problems faced by advertisers, especially by those who have little experience in Internet advertising, is configuring their campaigns in an efficient way. To configure a campaign properly it is required to select the appropriate target, so it is guaranteed a high acceptance of users to adverts. It is also required that the number of visits that satisfy the configuration requirements is high enough to cover the advertisers’ campaigns. Thus, this paper presents a novel methodology for optimizing the micro-targeting technique in direct response display advertising campaigns by using genetic algorithms as the basis optimization model and a machine-learning based click-through rate (CTR) model. We implement our methodology to optimize display advertising campaigns on mobile devices using a real dataset. Results show that our methodology is feasible to optimize the campaigns by selecting the set of the best features required. Also, customization of the advertising campaign selecting some features by an advertiser, e.g. applying micro-targeting, can be optimized efficiently.