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研究生:劉浚宇
研究生(外文):Chun-Yu Liu
論文名稱:手持裝置應用程式的排名方法和系統實作
論文名稱(外文):Methods and Systems for Ranking Mobile Application
指導教授:雷欽隆雷欽隆引用關係
口試委員:顏嗣鈞黃秋煌莊仁輝
口試日期:2015-07-24
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:30
中文關鍵詞:軟體市集手機應用程式排名系統機器人
外文關鍵詞:app marketmobileapplicationranking systemcrawler
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在現在的社會中,手機應用程式已經成為人們不可或缺的一項功能,其中一項很重要的因素是軟體市集的崛起,它使得應用程式的下載變得很方便。然而,這世界上已經存在著不少軟體市集,雖然大多數的人只知道Google商店和Apple的app store。手機應用程式可以透過不少方式做行銷,但就我們所知,最有效的行銷手法就是成為軟體市集的推薦。這是很顯然的,因為每個使用者想要下載手機應用程式都必須開啟軟體市集,而每開啟一次就是直接接受了推薦的應用程式作為廣告。另一方面,由於軟體市集是安裝應用程式的入口,因此想要找尋應用程式也會透過此。

在這篇論文中我們提出了一套方法來計算應用程式的熱門程度。我們認為每個應用程式都有它的衰退期,無論它曾是多熱門。因此排序應用程式的熱門程度不單只是其下載量,還要考慮它的衰退情形。這麼做是為了避免有些應用程式曾經風靡一時,卻因為其一時的下載量而歷久不衰,而其實它現在已經不是使用者所喜歡的了。
我們對Google Play商店的應用程式做統計,觀察下載量的變化,發現大多應用程式都有相似的成長量衰退情形,在不同分類的應用程式又有著不同的衰退速率,這對於手機應用程式的開發商是個很重要的數據分析,可以根據其應用程式種類隨著時間來達成預期的下載量。

In this decade, the popularity of the mobile Internet and the smart phones continue significant growth trend, because the immediacy, connectivity and convenience of the smartphones that are making many changes for the mobile internet user’s behavior, such easily installing/uninstalling the mobile applications (Apps) from the apps stores. Thus apps installing smartphones have already become necessities in daily life. There are many popular apps stores in the world, for example, Google Marketplace and Apple AppStore are the most well-known apps markets. The convenience of smartphone also brings a great change in the apps ecosystem, the downloading ratio of apps, the number of apps users and apps developers are significant increasing in recent years. How to market apps is becoming a critical and large part of mobile internet business. In order to market effectively, to become top-rated marketing apps is the best and the effective approach for the most apps developers. However, apps marketing is hard, the apps ranking and searching strategies are controlled and manipulated by the apps markets. There are few article examining the app ranking system, however the mobile app monetization strategy is dominating the entire mobile internet, for example, the revenue of apps reaches more than $38 billion in 2015.

In this paper, we propose a method to calculate the popularity of mobile applications. We think that no matter how popular they used to be, they own their recession. Therefore, it should not rank apps only by amount of downloads but consider the decays. It can avoid some situation that some applications used to be popular and earned lots of downloads, but actually not users’ favor now.
We collect the app data in Google Play store to make statistics, observe the change of downloads. In the experiment, we found most applications exist decay situation in volume growth. And different category of applications have their own decay velocities. This is a great analysis to developer of mobile applications, they can use to predict the downloads. App store can also calculate the real popularity.

誌謝 iii
摘要 iv
Abstract v
1 Introduction ................................... 1
2 Background ................................... 3
2.1 Movies ................................... 3
2.2 Books.................................... 4
2.3 Websites................................... 5
2.4 MobileApps ................................ 5
3 Approach ...................................7
3.1 SystemStructure .............................. 7
3.1.1 Network............................... 7
3.1.2 Database .............................. 8
3.1.3 SearchEngineforSearchingApps................. 10
3.2 Half-LifeMethod.............................. 11
4 Experiments ...................................18
4.1 Materials .................................. 18
4.2 Crawler ................................... 18
4.3 Experiments................................. 19
4.3.1 ExperimentI ............................ 20
4.3.2 ExperimentII&III......................... 23
5 Conclusion ......................... 28
Bibliography......................... 29

[1] Amazon web services (aws), a collection of remote computing services, also called web services, make up a cloud-computing platform offered by amazon.com. http: //aws.amazon.com/.
[2] App annie, the app analytics and app data industry. https://www.appannie.com/ cn/.
[3] Google analytics. http://www.google.com/analytics/.
[4] Googleplay,originallytheandroidmarket,isadigitaldistributionplatformoperated
by google. https://play.google.com/store.
[5] Imdb. http://www.imdb.com/.
[6] Metacritic. http://www.metacritic.com/.
[7] Rotten tomatoes. http://www.rottentomatoes.com/.
[8] Techcrunch, an online publisher of technology industry news. http://techcrunch. com/.
[9] Techorange. http://buzzorange.com/techorange/.
[10] Flurry, a mobile analytics, monetization, and advertising company. http://www.
flurry.com/, 2005.
[11] Apps solidify leadership six years into the mobile revolu- tion. http://flurrymobile.tumblr.com/post/115191864580/ apps-solidify-leadership-six-years-into-the-mobile, 2014.
29[12] Carare O. The impact of bestseller rank on demand: Evidence from the app market. INTERNATIONAL ECONOMIC REVIEW, 53(3):717–742, Jul 2012.
[13] MengCui,SongyunHu.Searchengineoptimizationresearchforwebsitepromotion.
Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference, 4(2):100–103, Sep 2011.
[14] Seung-Taek Park , David M. Pennock. Applying collaborative filtering techniques to movie search for better ranking and browsing. KDD ’07 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 7:550–559, Aug 2007.
[15] Shkapenyuk, V. , Suel, Torsten. Design and implementation of a high-performance distributed web crawler. Data Engineering, 2002. Proceedings. 18th International Conference, 18:357–368, Mar 2002.
[16] T. Petsas, A. Papadogiannakis, M. Polychronakis, E. P. Markatos, T. Karagiannis. Rise of the planet of the apps: a systematic study of the mobile app ecosystem. IMC ’13 Proceedings of the 2013 conference on Internet measurement conference, 13(13):277–290, Oct 2013.
[17] Tsuchiya K, Sugita M. A mathematical model for deriving the biological half-life of a chemical. Nordisk hygienisk tidskrift, 52(2):105–10, 1971.

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