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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲 | |
| dc.contributor.author | Yi-Ling Lin | en |
| dc.contributor.author | 林怡伶 | zh_TW |
| dc.date.accessioned | 2021-06-16T08:24:32Z | - |
| dc.date.available | 2014-01-27 | |
| dc.date.copyright | 2014-01-27 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2014-01-22 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58662 | - |
| dc.description.abstract | 近年來，智慧型手機變得相當普遍，而在相關產業當中，手機廣告是主要的獲益模式之一。有鑑於此，將適合的廣告推送給目標客群已經變成一項熱門且重要的研究議題。再者，我們認為使用者偏好與興趣會直接反應在手機軟體(app)的使用上，因此本研究特別探討手機軟體(app)在使用者點擊廣告時所扮演的角色，並透過分析不同類型app 的使用者群，讓廣告業者可以找到潛在的目標客群。另一方面，在廣告相關研究中，透過分析使用者行為並用以推薦使用者偏好的廣告是一大研究主流。然而對手機廣告而言，要如何將此概念應用在大量且快速增加的行動手機資料之上，在研究上仍是一項挑戰。
在本論文中，我們基於同時考慮使用者、app、廣告，提出創新的三階層分群系統，從使用者產生的大量行動資料中建立標籤式的廣告描述(profile)。分群系統會從廣告顯示與點擊的紀錄中，根據使用者使用手機軟體的喜好，以及廣告在各類手機廣告的點擊次數，產生彼此相依的使用者、app、廣告三種分群。除此之外，由於使用者行動資料每天持續快速且大量產生，為了實際應用的需求，我們在研究中採用漸進式分群系統用以快速更新分群結果。基於分群結果，標籤式廣告敘述建立系統會從使用者個人資訊中，計算出各分群最具代表性的標籤。最後我們將系統所產生的廣告標籤實際應用於廣告推薦的問題，實驗結果驗證了所提出系統具備高準確率，且其效能符合即時更新的需求。 | zh_TW |
| dc.description.abstract | Smart phones have become very ubiquitous in recent years, and in-app advertising is a primary business model for mobile applications. Following this trend, researchers are interested in how to provide appropriate ads to target users. In this thesis, we particularly focus on considering and analyzing the role of apps in ad-clicking behavior. We believe that user preference will be directly reflected on the apps they use. To capture the hidden interests of their target customers, advertisers should identify the characteristics of apps. On the other hand, adaptive profiling has been proposed to match user profiles and campaign profiles. However, when applying this concept to mobile advertising, how to produce profiles from tremendous amounts of daily mobile data is a challenging issue. We explore the problem of producing mobile ad profiles from mobile user-generated data and incorporate the usage log of apps into ad profiling. We propose a novel 3-layer clustering framework to realize the tagging-based mobile ad profiling. Moreover, since new usage logs will be generated continually, an incremental mechanism is also designed to address the update issue and provide up-to-date clustering results. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T08:24:32Z (GMT). No. of bitstreams: 1 ntu-102-R00921042-1.pdf: 1510426 bytes, checksum: 0d84e5e5a26ff208d5e9d26287a663b4 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 口試委員會審定書 .............................................i
Acknowledgements ........................................ ii 中文摘要 ................................................ iii ABSTRACT .................................................iv CONTENTS ................................................. v LIST OF FIGURES ........................................ vii LIST OF TABLES ........................................ viii Chapter 1 Introduction ................................... 1 Chapter 2 Problem Description and System Framework ....... 7 Chapter 3 Incremental 3-Layer Clustering ................ 10 3.1 3-layer Clustering ................................ 10 3.1.1 Layer1: User Clustering ....................... 10 3.1.2 Layer2: App Clustering ........................ 12 3.1.3 Layer3: Ad Clustering ......................... 13 3.2 Incremental Clustering ............................ 15 Chapter 4 Tagging-based Mobile Ad Profiling ............. 17 4.1 User Profile Generation ........................... 17 4.2 Tag Candidate Generation .......................... 19 4.3 Tag Weight Derivation based on Information Gain ... 20 Chapter 5 Experiment .................................... 25 5.1 Experimental Design ............................... 25 5.2 Case Study ........................................ 26 5.2.1 Tagging-based Profiles ........................ 26 5.2.2 Difference between Weekday and Weekend ........ 28 5.3 Performance Comparison ............................ 29 5.3.1 Efficiency of Incremental User Clustering ..... 29 5.3.2 Effectiveness of 3-layer Clustering ........... 31 5.4 Parameter Studies ................................. 32 Chapter 6 Conclusion .................................... 35 Chapter 7 Related Work .................................. 36 REFERENCES .............................................. 38 | |
| dc.language.iso | en | |
| dc.subject | 手機廣告 | zh_TW |
| dc.subject | 廣告剖析 | zh_TW |
| dc.subject | 分群演算法 | zh_TW |
| dc.subject | 漸進式更新 | zh_TW |
| dc.subject | 使用者行為定位 | zh_TW |
| dc.subject | Ad Profiling | en |
| dc.subject | Incremental Update | en |
| dc.subject | User Behavior Targeting | en |
| dc.subject | Mobile Advertisement | en |
| dc.subject | Clustering Algorithm | en |
| dc.title | 建構標籤式行動廣告的漸進式三階層分群系統 | zh_TW |
| dc.title | Constructing Tagging-based Mobile Ad Profiling: An Incremental 3-layer Clustering Framework | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林永松,呂俊賢,陳正君,曾祺堯 | |
| dc.subject.keyword | 手機廣告,廣告剖析,分群演算法,漸進式更新,使用者行為定位, | zh_TW |
| dc.subject.keyword | Mobile Advertisement,Ad Profiling,Clustering Algorithm,Incremental Update,User Behavior Targeting, | en |
| dc.relation.page | 42 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-01-23 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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