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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
| dc.contributor.author | SHANG-CHI LI | en |
| dc.contributor.author | 李尚錡 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:27:30Z | - |
| dc.date.available | 2022-09-01 | |
| dc.date.available | 2022-11-24T03:27:30Z | - |
| dc.date.copyright | 2021-11-11 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-25 | |
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Apple’s App Store and Google Play Users Spent over $83 Billion on Mobile Apps in the Last 12 Months, Globally. https://www.digitalinformationworld.com/2020/01/global-consumers-spent-over-83-billion-on-mobile-apps-in-the-last-12-months.html Salton, G., Wong, A., Yang, C.-S. (1975). A Vector Space Model for Automatic Indexing. Communications of the ACM, 18(11), 613-620. Simonyan, K., Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556. Su, J., Cao, J., Liu, W., Ou, Y. (2021). Whitening Sentence Representations for Better Semantics and Faster Retrieval. arXiv preprint arXiv:2103.15316. Sulleyman, A. (2017, November 6). Fake Whatapps App Tricks More Than a Million People into Downloading It. https://www.independent.co.uk/life-style/gadgets-and-tech/news/whatsapp-app-android-messaging-apps-fake-google-play-a8039806.html Sun, M., Li, M., Lui, J. C. (2015). DroidEagle: Seamless Detection of Visually Similar Android Apps. Proceedings of the 8th ACM Conference on Security Privacy in Wireless and Mobile Networks, 1-12. Viennot, N., Garcia, E., Nieh, J. (2014). A Measurement Study of Google Play. The 2014 ACM International Conference on Measurement and Modeling of Computer Systems, 221-233. Wang, Q., Li, B., Singh, P. V. (2018). Copycats Vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis. Information Systems Research, 29(2), 273-291. Wikipedia contributors. (2021). App Store Optimization. Wikipedia, The Free Encyclopedia. Retrieved 18 April from https://en.wikipedia.org/w/index.php?title=App_store_optimization oldid=1000140122 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81039 | - |
| dc.description.abstract | 行動應用程式是現代人生活中不可或缺的一部分，有助於提高工作效率以及提供日常娛樂。儘管行動應用程式商店的程式數量以及總營收皆逐年成長，並非所有程式開發商都能獲得豐厚的收入。根據統計，美國市場的前二十五大開發商已占據平台一半的總營收。換言之，大部分的開發商僅能取得一小部分的營收。 為了使行動應用程式具有高知名度，程式開發商應要關注其應用程式的主要競爭者。根據App Store的統計，65%的下載行為發生於搜尋行為之後，而同時出現在相關關鍵字搜尋結果的競爭者則被稱作「搜尋結果頁面競爭者」 (Search Engine Results Pages Competitors)。雖然搜尋結果頁面競爭者應與行動應用程式的下載量息息相關，至今仍缺乏相關的實證證據闡明搜尋流量如何影響行動應用程式的需求量。 本篇研究透過蒐集Google Play Store的每日下載資料，檢驗搜尋結果頁面競爭者的影響。此外，為了檢驗競爭者之功能性以及外觀相似度是否對需求量造成不同影響，我們採用深度學習的文字以及圖片向量以比較行動應用程式的敘述以及圖示的相似度。結果顯示當一個應用程式出現在知名應用程式的結果搜尋頁中，對其需求量具有正面影響。若是該應用程式具有較高的評分，或是與知名應用程式的外觀不相似，將會增強此正面影響。在功能相似度方面，則是根據所屬類別而有所不同。本篇研究之貢獻為提供實證證據，以闡明行動應用程式平台中的關鍵字競爭。此實證證據足以協助開發商之策略決策，例如是否投入成本於關鍵字優化，以及如何辨別競爭之應用程式等議題。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:27:30Z (GMT). No. of bitstreams: 1 U0001-2408202117243400.pdf: 1790775 bytes, checksum: 57e05429296e3938f6b7185f24ec84b8 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 中文摘要 i ABSTRACT ii CONTENTS iii LIST OF FIGURES v LIST OF TABLES vi Chapter 1 Introduction 1 Chapter 2 Literature Review 3 2.1 Competition between Mobile Apps 4 2.1.1 Factors in App Downloads 4 2.1.2 Ranking Factors in App Markets 6 2.1.3 Utilitarian and Hedonic Apps 6 2.2 Techniques for Searching Similar Apps 8 2.2.1 Similar App Detection Framework 8 2.2.2 Text Representation 12 2.2.3 Image Representation 15 2.3 Summary of Literature Review 17 Chapter 3 Data 19 Chapter 4 Methodology 21 4.1 Estimating Daily Downloads 21 4.2 Rank Estimation for Unranked Apps 24 4.3 Evaluating Competitors’ Quality 25 4.4 Detecting Functional Similarity 25 4.5 Detecting Appearance Similarity 27 4.6 Evaluation of Detection Methods 28 4.7 Empirical Model 29 Chapter 5 Result Discussion 32 5.1 Estimating Daily Downloads 32 5.2 Rank Estimation for Unranked Apps 33 5.3 Detecting Functional Similarity 36 5.4 Detecting Appearance Similarity 36 5.5 Empirical Model 37 Chapter 6 Conclusion 50 REFERENCE 52 | |
| dc.language.iso | en | |
| dc.subject | 欺騙性程式 | zh_TW |
| dc.subject | 行動應用程式 | zh_TW |
| dc.subject | 搜尋流量 | zh_TW |
| dc.subject | 需求量分析 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 程式功能相似 | zh_TW |
| dc.subject | app function similarity | en |
| dc.subject | deceptive apps | en |
| dc.subject | mobile apps | en |
| dc.subject | search traffic | en |
| dc.subject | demand analysis | en |
| dc.subject | deep learning | en |
| dc.title | 行動應用程式之搜尋競爭：相似度檢測與下載量分析 | zh_TW |
| dc.title | Search Competition Between Mobile Apps: Analyzing Similar Apps and Their Downloads | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 顏如君(Hsin-Tsai Liu),簡宇泰(Chih-Yang Tseng) | |
| dc.subject.keyword | 行動應用程式,搜尋流量,需求量分析,深度學習,程式功能相似,欺騙性程式, | zh_TW |
| dc.subject.keyword | mobile apps,search traffic,demand analysis,deep learning,app function similarity,deceptive apps, | en |
| dc.relation.page | 56 | |
| dc.identifier.doi | 10.6342/NTU202102689 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-08-27 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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