The document discusses user interaction measurement in mobile apps, highlighting the importance of understanding usage patterns to provide customized app recommendations. It reviews the influence of contextual factors like time and location on app choices and outlines how data from sources like Appazaar and Appsensor can be utilized for better insights into user behavior. Additionally, it covers the clustering effects of apps and their popularity based on user engagement and downloading patterns.

1. Mobile apps-user interaction measurement
Prepared by
Salah Amean Ahmmed Saeed
Source: 1-Falling Asleep with Angry Birds, Facebook and
Kindle –
A Large Scale Study on Mobile Application Usage, by Matthias
Böhmer
&
Apps ecosystem
Presented at
Prof. Choi Kae Won-Lab
1

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 apps
according to the contextual knowledge
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4. User Behavour
4
 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 their
users?

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 specific
time
 So far there is no user behavour study
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6. Contextual description
 How does the user’s context – e.g. location and time of day – affect
her app choices?
 What type of app is opened first?
 Does the opening of one application predict the opening of
another?
 The interest of Böhmer et al.:
 Is to provide data from a large-scale study that begins
 to answer these basic app usage questions (especially those related to
contextual usage.)
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7. Appazaar and AppSensor
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 Previous needs are translated as Usage pattern which are
gathered by Appazaar
 Which was studied by Böhmer et al
 Is used to recommend apps based on the Appstores gathered data
 Based on the user’s current and past locations and app usage, the
system recommends apps that might be of interest to the user.
 Within the Appazaar app AppSensor, that does the job vital to this
research of measuring which apps are used in which contexts.

8. Capture of Appazaar
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9. Life cycle of mobile app
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 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 cycle
10
 Since android OS system can report the most recently started
application
 They enable us to observe app usage on a more fine-grained level
,and
 Provide a much more accurate understanding of context’s effects on
app usage.
 This OS report functionalities to be captured by the AppSensor

11. Formal Description of AppSensor
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 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 in
the App store
 AppSensor provide the values:
 AppSensor can know when a certain app is being used if
 If app is change then the

12. Re
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13. Recruiting app users
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 https://spreadsheets.google.com/viewform?hl=de&formkey=dEtKRz
djb1N4djVjYlhzNmw5SVlGWmc6MA
 Users have the choice to allow installation


14. Application chains
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15. 15

16. Apps usage in the Whole day vs one time
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17. 17

18. Mobile Ecosystem -2nd paper
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 Mobile phones have gained a lot of popularity because of the
continues 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 Ecosystem
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 Analysis of Apps popularity
 Measurement of Apps popularity
 Modeling Apps popularity
 Explore pricing

20. Zipf Distribution- Wikipedia
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 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 word’s 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 word
frequency
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22. What is the significance of Zipf?
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 Apps download partially follows Zipf
 There is a deviation from the Zipf model
 Due too clustering effect

23. Clustering Effect
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24. Pareto Effect-wiki
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 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 crashes
would 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 popularity
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26. Data collection
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 Data are gathered from four android market place
 AppChina and Anzhi
 are very popular appstores located in China, with more than 50,000
apps each

27. Data collection
27
 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 and
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29. Clustering
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 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 affinity
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 To validate the clustering effect
 This study is based on users comments and rating gathered from
Anzhi Appstore
 During the crawling process from the Appstore,
 stream of comments were recorded for each user on each app
 They suppressed successive comments so
if(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 푎푖
 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,… 푐푛
 with respective n comments for the user( in chronological order)

31. 31
Temporal affinity- continued
 We define Temporal affinity metrics Aff as the number of elements
in 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 on
App 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. 32
Temporal 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. 33
Temporal affinity- Random walk
 In practice, App are not evenly distributed among categories
 calculate the accurate affinity probability of a random walk in the
Anzhi marketplace
 They used the actual distribution of apps to the C different
categories
 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. 34
Temporal affinity- Random walk-continue
 퐴푓푓푟푎푛푑표푚 푤푎푙푘 , i.e., the probability that two random app choices
belong 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 the
same category is

35. User behavour based on APP-Clustering
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 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 same
cluster c of a previously downloaded app.
 The cluster c is randomly chosen from previous downloads with a
uniform 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 user’s downloads are less than d go to 2.

36. 36
 Predicted downloads for app with total rank i and rank j in its
cluster:
 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-comparison
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38. Pricing
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 Universal fact 
 People prefer free stuff
 Free Apps are more popular than free ones

39. End...
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