Intelligent Digital Signage-based Advertisement Scheduling Algorithms for Impression Maximization Problem

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 105 === In recent years, the digital signage industry has experienced tremendous growth, so the topic of digital signage-based advertisement scheduling become hotter. In this paper, we study a impression maximization problem for advertisement scheduling on digital si...

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Main Authors: Wu, Hao-Dong, 吳昊東
Other Authors: Peng, Wen-Chih
Format: Others
Language:en_US
Published: 2016
Online Access:http://ndltd.ncl.edu.tw/handle/62109047302425086364
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spelling ndltd-TW-105NCTU53940302017-09-06T04:22:26Z http://ndltd.ncl.edu.tw/handle/62109047302425086364 Intelligent Digital Signage-based Advertisement Scheduling Algorithms for Impression Maximization Problem 基於智能化電子看板的針對觀看人數最大化問題的排程演算法設計 Wu, Hao-Dong 吳昊東 碩士 國立交通大學 資訊科學與工程研究所 105 In recent years, the digital signage industry has experienced tremendous growth, so the topic of digital signage-based advertisement scheduling become hotter. In this paper, we study a impression maximization problem for advertisement scheduling on digital signages. We train an AD-model to make our system more intelligent to help our scheduling. We proposed two sets of advertisement scheduling algorithms, probability-based scheduling and AD-Pair scheduling. Our probability-based scheduling algorithms help us to save much time to schedule the playing order. And the AD-Pair scheduling algorithms groundbreakingly bind two Ads together as a playing unit. In the situation that there are not many, even any strong Ads, we creatively add some strong Ads (we call them "Attractions") into the advertisements set given by advertisers, to attract more people to watch the signage. We have conducted comprehensive experiments on synthetic datasets, and the experimental results show that the proposed models are suitable for effective impression maximization in digital signage advertising. Peng, Wen-Chih 彭文志 2016 學位論文 ; thesis 35 en_US
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language en_US
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description 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 105 === In recent years, the digital signage industry has experienced tremendous growth, so the topic of digital signage-based advertisement scheduling become hotter. In this paper, we study a impression maximization problem for advertisement scheduling on digital signages. We train an AD-model to make our system more intelligent to help our scheduling. We proposed two sets of advertisement scheduling algorithms, probability-based scheduling and AD-Pair scheduling. Our probability-based scheduling algorithms help us to save much time to schedule the playing order. And the AD-Pair scheduling algorithms groundbreakingly bind two Ads together as a playing unit. In the situation that there are not many, even any strong Ads, we creatively add some strong Ads (we call them "Attractions") into the advertisements set given by advertisers, to attract more people to watch the signage. We have conducted comprehensive experiments on synthetic datasets, and the experimental results show that the proposed models are suitable for effective impression maximization in digital signage advertising.
author2 Peng, Wen-Chih
author_facet Peng, Wen-Chih
Wu, Hao-Dong
吳昊東
author Wu, Hao-Dong
吳昊東
spellingShingle Wu, Hao-Dong
吳昊東
Intelligent Digital Signage-based Advertisement Scheduling Algorithms for Impression Maximization Problem
author_sort Wu, Hao-Dong
title Intelligent Digital Signage-based Advertisement Scheduling Algorithms for Impression Maximization Problem
title_short Intelligent Digital Signage-based Advertisement Scheduling Algorithms for Impression Maximization Problem
title_full Intelligent Digital Signage-based Advertisement Scheduling Algorithms for Impression Maximization Problem
title_fullStr Intelligent Digital Signage-based Advertisement Scheduling Algorithms for Impression Maximization Problem
title_full_unstemmed Intelligent Digital Signage-based Advertisement Scheduling Algorithms for Impression Maximization Problem
title_sort intelligent digital signage-based advertisement scheduling algorithms for impression maximization problem
publishDate 2016
url http://ndltd.ncl.edu.tw/handle/62109047302425086364
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