1. Mobile apps-user interaction measurementPrepared bySalah Amean Ahmmed SaeedSource: 1-Falling Asleep with Angry Birds, Facebook andKindle A Large Scale Study on Mobile Application Usage, by MatthiasBhmer&Apps ecosystemPresented atProf. Choi Kae Won-Lab1

2. Swiss Army knife Communication Social networking Productivity Sports News Games Settings Browsing Travel Shopping Finance Camera: video, etc Etc.2 3. Big Deal ? Millions of Devices require apps. Apps require developers Developers Need to be paid Apps need to be stored somewhere where Huge demand on knowledge of usage behavior(why?) Essential for understanding usage pattern In order to customize services Provide specific users with specific apps recommendations This knowledge could be utilized by app stores to cluster appsaccording to the contextual knowledge3 4. User Behavour4 How long does each interaction with an app last? Less than a minute More than a minute and less than 5 minutes Does this vary by application category? Social networking versus alarm or weather app If so, which categories inspire the longest interactions with theirusers? 5. Contextual description Example: News app is highly probable to be checked morning Games in the evening From this we know that certain apps are likely to be used at specifictime So far there is no user behavour study5 6. Contextual description How does the users context e.g. location and time of day affecther app choices? What type of app is opened first? Does the opening of one application predict the opening ofanother? The interest of Bhmer et al.: Is to provide data from a large-scale study that begins to answer these basic app usage questions (especially those related tocontextual usage.)6 7. Appazaar and AppSensor7 Previous needs are translated as Usage pattern which aregathered by Appazaar Which was studied by Bhmer et al Is used to recommend apps based on the Appstores gathered data Based on the users current and past locations and app usage, thesystem recommends apps that might be of interest to the user. Within the Appazaar app AppSensor, that does the job vital to thisresearch of measuring which apps are used in which contexts. 8. Capture of Appazaar8 9. Life cycle of mobile app9 Determined by five states: When a user Needs and App, then the user Install it Remove it, if the user does not like it. The app has two states in the eyes of users Used or in the background Switching is also an important factor to consider weather to games Weather to communication Updates often are done automatically and most Apps do not update frequently 10. What is the benefits of App life cycle10 Since android OS system can report the most recently startedapplication They enable us to observe app usage on a more fine-grained level,and Provide a much more accurate understanding of contexts effects onapp usage. This OS report functionalities to be captured by the AppSensor 11. Formal Description of AppSensor11 Let A is the available application on a devices And all the application the user can interact with Where A is the application in the devices and e is the rest of apps inthe App store AppSensor provide the values: AppSensor can know when a certain app is being used if If app is change then the 12. Re12 13. Recruiting app users13 https://spreadsheets.google.com/viewform?hl=de&formkey=dEtKRzdjb1N4djVjYlhzNmw5SVlGWmc6MA Users have the choice to allow installation 14. Application chains14 15. 15 16. Apps usage in the Whole day vs one time16 17. 17 18. Mobile Ecosystem -2nd paper18 Mobile phones have gained a lot of popularity because of thecontinues advancement of telecommunication Technologies 640 million device (active July 2012) Apps for android seven fold increase Hundreds of thousands of Apps on the App stores This huge number, urges: Understanding about the users grouping Providing a systematic approach for pricing 19. Motivation of App Ecosystem19 Analysis of Apps popularity Measurement of Apps popularity Modeling Apps popularity Explore pricing 20. Zipf Distribution- Wikipedia20 Brown Corpus The most frequently occurring word ( 7% out million 69,971)the Then the second most frequent is half (3.5%344985.5) x is rank of a word in the frequency table; (diagram in next slide) y is the total number of the words occurrences. Most popular words are "the", "of" and "and", Zipf's law corresponds to the upper linear portion of the curve 21. Zipf Distribution for Wikipedia wordfrequency21 22. What is the significance of Zipf?22 Apps download partially follows Zipf There is a deviation from the Zipf model Due too clustering effect 23. Clustering Effect23 24. Pareto Effect-wiki24 80% of a company's complaints come from 20% of its customers 80% of a company's sales are made by 20% of its sales staff Microsoft noted that: Fixing the top 20% most reported bugs, 80% of the errors and crasheswould be eliminated 90% of apps download comes for only 10% of Apps( Really ) Big chance that users in same area tends to use same apps Sharing of new and good apps Trusting others review make a big difference 25. Apps popularity25 26. Data collection26 Data are gathered from four android market place AppChina and Anzhi are very popular appstores located in China, with more than 50,000apps each 27. Data collection27 Data are gathered from four android market place AppChina and Anzhi (Chinese) with more than 50,000 apps each SlideMe is one of the oldest Android marketplaces, founded in 2008,containing more than 20,000 apps 1Mobile is one of the largest third-party appstores, with more than 150,000 apps. 28. Data collection and28 29. Clustering29 Appstores are organized into groups of related apps E-book News Games Etc. User focus on a single or few categories Downloading news app and ignore finance app This implies the clustering effect App download is affected by the popularity of this App Number of downloads, comments E.g., one could download few games more than tools 30. Temporal affinity30 To validate the clustering effect This study is based on users comments and rating gathered fromAnzhi Appstore During the crawling process from the Appstore, stream of comments were recorded for each user on each app They suppressed successive comments soif(a1,a2,a3,a3,a1,a4)(a1,a2,a3,a4) (a1,a2,a3,a4) is called App string In the Appstores, there are categories like So (a1,a2,a3,a3,a1,a4) is categorized into category string c(a1), c(a2),c(a3), c(a4). S is String category of n elements c1,c2,c3, with respective n comments for the user( in chronological order) 31. 31Temporal affinity- continued We define Temporal affinity metrics Aff as the number of elementsin the same category, with the previous element divided by n-1 from the formula if Aff is 1, then they are in the same category Example: category string c1c1c1c1, the Aff is 3/3, the user tends to comment onApp from the same category string c1c1c1c2, the Aff is 2/3 string c1c1c2c3, the Aff is 1/3 user switched from category to another 32. 32Temporal affinity- continued Pattern like c1,c2,c1,c2 are solving using affinity depth To make sure all the elements from the same category Are classified together 33. 33Temporal affinity- Random walk In practice, App are not evenly distributed among categories calculate the accurate affinity probability of a random walk in theAnzhi marketplace They used the actual distribution of apps to the C differentcategories Let A be the total number of apps in the appstore A(i) the number of apps that belong to category i. Given this distribution, the random walk affinity probability Continued.. 34. 34Temporal affinity- Random walk-continue , i.e., the probability that two random app choicesbelong to the same category, is equal to: A*(A -1) possible random app choices The number of app choices where these two apps belong to thesame category is 35. User behavour based on APP-Clustering35 1. Download the first app according to the ZG distribution. 2. Download another app: 2.1. with probability p the app will be downloaded from the samecluster c of a previously downloaded app. The cluster c is randomly chosen from previous downloads with auniform probability. The app from cluster c is drawn from distribution Zc. If the app has been downloaded go to 2.1. 2.2. with probability 1 p the app will be drawn from ZG. If the app has been downloaded go to 2.2. 3. If users downloads are less than d go to 2. 36. 36 Predicted downloads for app with total rank i and rank j in itscluster: The overall downloads for an app equal to The probability of all users downloading this app For a single user equals: The probability 1-(zipf based(1-p)*d) times p*d cluster 37. Simulation 3 models-comparison37 38. Pricing38 Universal fact People prefer free stuff Free Apps are more popular than free ones 39. End...39