資料科學在 Whoscall 產品體系中的角色

1. Gogolook Confidential

2. Gogolook Confidential How Started?

5. Gogolook Confidential The Best App For identifying and blocking calls The Best App –LINE whoscall

6. Gogolook Confidential

7. Gogolook Confidential KeyFeatures

8. Gogolook Confidential ★Instant Caller Identification LINE whoscallidentifies background information of incoming unknown calls in seconds through tags reported by other users, Internet search results, and our comprehensive global database. Instant Caller Identification

9. Gogolook Confidential ★Database with over 600Million Phone Numbers LINE whoscallboasts an online database with over 600 million phone numbers. The database of LINE whoscallcovers yellow pages, spammers, telemarketers, costumer services...,etc. with numerous community tags contributed by users and comments based on real users’ experiences. Database & Number Details

10. Gogolook Confidential Incoming Call Dialogue Incoming Call Dialogue Fraud Call Business Corporation Restaurant

11. Gogolook Confidential ★Community Tag ★Block unwanted calls & SMSs Contributions from the global user community has always been the pillar of LINE whoscall’sservice. LINE whoscalluser can tag aphone number and share it with others, which creates an integrated phone number database and a reliable communication network for everyone. Block calls and SMSs intelligently to ensure a harassment-free calling experience. Tag & Block

12. Gogolook Confidential ★World’s Largest Yellow Page Database ★Offline Database Available for Free LINEwhoscallowns one of the world’s largest onlinephonenumber database in the world, which covers most of numbers of businesses and service providers essential to you daily lives. The free database is not only available online but also offline. And they are completely free! The unlimited usage of database with over 600 million phone numbers is only on LINE whoscall. Database Usage

13. Gogolook Confidential 3 of every 5 strangers’ calls can be identified by LINE whoscall Over 400 million phone calls are identified by LINE whoscallevery month. 3000 spammer numbers are reported by LINE whoscalluser every day. Number Identification –2014.07 –2014.07

14. Gogolook Confidential Market

15. Gogolook Confidential Honors

16. Gogolook Confidential What we will be…

17. Gogolook Confidential Vision

18. Gogolook Confidential 資料科學在whoscall的應用 GOGOLOOK資料科學家高義銘

19. Gogolook Confidential ★日常生活經常會遇到的問題

20. Gogolook Confidential ★人面對未知的事物就會有一種… 我有一種不祥的預感!

21. Gogolook Confidential ★坊間流傳著許多解決此問題的APPs 小熊來電通知

22. Gogolook Confidential ★坊間流傳著許多解決此問題的APPs 小熊來電通知

23. Gogolook Confidential ★Why whoscall? 因為…他是連Google執行長都說讚的軟體! 唉呦，讚喔

24. Gogolook Confidential whoscall是如何解決未知來電的問題咧?

25. Gogolook Confidential ★Technologies adopted 1. Yellow pages: HiPage, Yelp, Zenrin… 2. Google search 3. Other sources Technologies adopted

26. Gogolook Confidential ★Technologies adopted Technologies adopted 4. 使用者回報與標記

27. Gogolook Confidential ★Technologies adopted Technologies adopted 4. 使用者回報與標記

28. Gogolook Confidential ★whoscall, I have a problem… 如果一個未知號碼，我們無法從這些sources 去取得任何資訊，那就GG 了嗎?

29. Gogolook Confidential ★whoscall, I have a problem… 如果一個未知號碼，我們無法從這些sources 去取得任何資訊，那就GG 了嗎? 是的，GG然後洗洗睡…

30. Gogolook Confidential 當然不能洗洗睡，要不然我站在這邊幹嘛?

31. Gogolook Confidential ★Problem we want to solve For an unknown phone number: • No google result • No user tag / report • Not a whoscalluser Problem we want to solve

32. Gogolook Confidential ★Problem we want to solve For an unknown phone number: • No google result • No user tag / report • Not a whoscalluser Can we determine if it’s a spamnumber? Problem we want to solve

33. Gogolook Confidential ★Problem we want to solve For an unknown phone number: • No google result • No user tag / report • Not a whoscalluser Can we determine if it’s a spam number? 推銷電話? Problem we want to solve

34. Gogolook Confidential ★Problem we want to solve For an unknown phone number: • No google result • No user tag / report • Not a whoscalluser Can we determine if it’s a spam number? 推銷電話? 詐騙電話? 騷擾電話? Problem we want to solve

35. Gogolook Confidential ★Problem we want to solve For an unknown phone number: • No google result • No user tag / report • Not a whoscalluser Can we determine if it’s a spam number? 推銷電話? 詐騙電話? 騷擾電話? 打錯電話? Problem we want to solve

36. Gogolook Confidential ★Problem we want to solve For an unknown phone number: • No google result • No user tag / report • Not a whoscalluser Can we determine if it’s a spam number? 推銷電話? 詐騙電話? 騷擾電話? 打錯電話? Problem we want to solve (我又不是神!!)

37. Gogolook Confidential ★Scenario Scenario OO推銷小明小明妹小明哥 ?

38. Gogolook Confidential ★We think it should work because… whoscalluserbase( = potential sensors) • > 10 million installations • > 10 thousands tags (daily) • > 30 million phone calls (daily)

39. Gogolook Confidential Analysis procedures Analysis procedures 1. Collect call logs 2. Compare with user tags 3. Explore call behaviors 4. Extract features 5. Classify unknown numbers using machine learning techniques

40. Gogolook Confidential ★Collect call logs • Recruit a group of voluntary whoscallusers as our sensors. • Collect phone call logs from these sensors for a month. Collect call logs

41. Gogolook Confidential ★User privacy  User privacy is kept in the highest priority.  Phone numbers are stored as one-way hash codes. (therefore unable to be reversed) User privacy

43. Gogolook Confidential ★List of user tags List of user tags 一接就掛斷一打來就掛掉一接對方馬上掛斷一接就掛電話一接起來就掛斷電話一接起來，就說打錯一直傳廣告簡訊一直打錯電話一直收到沒顯示的 APP 一直狂打錯電話一聲一聲不響，就掛掉，有問題一聲就掛一聲掛斷一聽收線嚴重騷擾國外莫名來電國際電話偽裝台北碼??? 地下錢莊地下錢莊推銷地下非法期公司地產垃圾垃圾簡訊垃圾訊息基隆美髮壽險外勞夜半打給不認識的在亂色情交友色情交友電話色情人肉市場色情垃圾簡訊色情外送色情妹妹電話色情干擾色情廣告簡訊色情拉客妹色情按摩色情推銷色情推銷電話色情援交外送色情敗類摩門撥了馬上掛掉擾亂電話收數率調查收視率調查放款簡訊政府宣導敲一聲而已整人電話新光保全星展借貸星展推消星展銀行淫媒仲介

44. Gogolook Confidential ★Compare with user tags • Compare these phone numbers with user reports from whoscalldatabase (封鎖記錄) Compare with user tags Normal numbers 0987-991-XXX 0986-225-XXX 02-2675-XXXX 03-862-XXXX ... 02-2543-XXXX 03-556-XXXX 886-XXXX … 推銷電話 02-2783-XXXX 886-903-XXXX 0800-000-XXX … 惡意電話

45. Gogolook Confidential ★Data summary Data summary 推銷電話民調中心騷擾電話詐騙電話 70% 1% 5% 24% # Samples: 7854 Normal: 4000 Spam: 3854

47. Gogolook Confidential Normal numbers 0 5 10 15 20 Calls =195 (in 66, out 129) Opponents = 72 (in 21, out 58) ★Normal numbers

48. Gogolook Confidential ★Spam numbers Spam numbers 0 10 20 30 Calls =471 (in 15, out 456) Opponents = 186 (in 11, out 183) XX信用卡行銷(7) OOO，XXXX行銷(6) 電話行銷(3)

50. Gogolook Confidential ★What is a feature? What is a feature? “Feature”is a measurable property of a phenomenon being observed.

51. Gogolook Confidential Example Or, we want to analyze a company, we can look at features: 公司人數 ★Example

52. Gogolook Confidential Example Or, we want to analyze a company, we can look at features: 工程師人數 ★Example

53. Gogolook Confidential Example Or, we want to analyze a company, we might look at features: 公司裡面 Python工程師的比例 ★Example

54. Gogolook Confidential Example Or, we want to analyze a company, we might look at features: 公司向心力 ★Example

55. Gogolook Confidential Example Or, we want to analyze a company, we might look at features: CEO帥氣程度 ★Example

56. Gogolook Confidential Features for call patterns Ratio of out calls 0.8 0.6 0.4 0.2 0.0 Fraud Marketing Normal

57. Gogolook Confidential Features for call patterns Ratio of recurring opponents Fraud Marketing Normal 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

58. Gogolook Confidential Features for call patterns Ratio of missed out calls Fraud Marketing Normal 0.6 0.5 0.4 0.3 0.2 0.1 0

59. Gogolook Confidential Features for call patterns Ratio of working time calls Fraud Marketing Normal 0.6 0.5 0.4 0.3 0.2 0.1 0 0.7

60. Gogolook Confidential Features for call patterns Median of call durations Fraud Marketing Normal 50 40 30 20 10 0 60 seconds

61. Gogolook Confidential Features for call patterns Ratio of out calls in contact book Fraud Marketing Normal 0.10 0 0.25 0.30 0.35 0.20 0.15 0.05

63. Gogolook Confidential Ratio of recurring components is less than 40% Ratio of out calls is more than 60% Ratio of in calls is less than 20% Then we claim the number is a spam number Intuitively, we can determine an unknown number by rules such as if ★Naïve method

64. Gogolook Confidential ★Problem 1 Too many features…

65. Gogolook Confidential ★Problem 2 How to determine the rule?

66. Gogolook Confidential Machine learning ★Solution

67. Gogolook Confidential Machine learning ★Solution Let the machine learn from the data

68. Gogolook Confidential What is machine learning? ★What is machine learning? 機器學習是一種從過去的資料或經驗當中，構造一個模型(Model)，而學習(Learning)這件事就是讓這個模型以程式的方式執行，等到學習到一定的程度後，就可以做預測(猜)，這個「猜」是有根據的，且命中率高的。

69. Gogolook Confidential Machine learningtechniques for classification ★Machine learning techniques for classification Support vector machine Logistic regression Decision tree Neural networks Naïve Bayes Nonparametric Bayesian method

70. Gogolook Confidential Support vector machine for binary classification ★Support vector machine for binary classification

78. Gogolook Confidential 這樣就夠了嗎？

79. Gogolook Confidential Real-life scenario ★Real-life scenario When will we require a spam number prediction? Ans: The time a phone call reaches a whoscalluser We want to predict whether a number is spam as EARLYas possible in order to prevent further victims…

80. Gogolook Confidential Real-life scenario Time #recent calls Victim 1 Victim 2 Victim 3 XX推銷 ★Real-life scenario 推銷電話

81. Gogolook Confidential Let’s look at the performances of SVM under different numbers of recent calls

82. Gogolook Confidential SVM for binary classification ★SVM for binary classification 0.8 0.85 0.9 0.95 1.0 3 4 5 6 7 8 9 10 # recent calls Accuracy

83. Gogolook Confidential 嗯…表現的不錯，但是… 可以再快一點嗎?

84. Gogolook Confidential Reduce the number of features ★Reduce the number of features Features computation is time-consuming. So we want to reduce the number of features before we do classification.

85. Gogolook Confidential Reduce the number of features ★Reduce the number of features Features computation is time-consuming. So we want to reduce the number of features before we do classification. 當然我們不是用手去選…

86. Gogolook Confidential Reduce the number of features ★Reduce the number of features Features computation is time-consuming. So we want to reduce the number of features before we do classification. Feature selection methods: Regularization methods Backward, forward, and stepwise methods Bayesian feature selection Random forest method

87. Gogolook Confidential Feature selection results ★Feature selection results 10 15 20 25 30 3recent calls 5recent calls 10 recent calls 0.8 0.85 0.9 0.95 1.0 # features Accuracy

90. Gogolook Confidential Ratio of out calls Rate of out calls Ratio of out calls in contact book Ratio of reciprocal opponents Ratio of recurring opponents Median call duration of in calls Ring duration of answered calls and more… ★Selected features Ratio of missed calls Rate of new opponents Ratio of in calls in contact book

91. Gogolook Confidential ★Comparison of w/ and w/o feature selection 0.8 0.85 0.9 0.95 1.0 3 4 5 6 7 8 9 10 # recent calls Accuracy

92. Gogolook Confidential Done? 阿不就好棒棒?

93. Gogolook Confidential What is power? ★What is power? Power of class A: The probability of accurately classify a class A sampleto class A.

94. Gogolook Confidential What is power? ★What is power? Power of class A: The probability of accurately classify a class A sampleto class A. 性別 Classifier 97.5% this is a male

95. Gogolook Confidential What is power? ★What is power? Power of class A: The probability of accurately classify a class A sampleto class A. 性別 Classifier 97.5% this is a male

96. Gogolook Confidential Power of our classifier ★Power of our classifier 0.8 0.85 0.9 0.95 1.0 3 4 5 6 7 8 9 10 #recent calls Power

97. Gogolook Confidential 義銘，加油好嗎?

100. Gogolook Confidential Marketing numbers vs. normal numbers ★Marketing numbers vs. normal numbers 0.8 0.85 0.9 0.95 1.0 3 4 5 6 7 8 9 10 #recent calls Accuracy

101. Gogolook Confidential Fraud numbers vs. normal numbers ★Fraud numbers vs. normal numbers 0.8 0.85 0.9 0.95 1.0 3 4 5 6 7 8 9 10 #recent calls Accuracy

102. Gogolook Confidential 一種摻在一起做撒尿牛丸的概念…

103. Gogolook Confidential Power of SVMfor multi-classification ★Power of SVM for multi-classification 0.8 0.85 0.9 0.95 1.0 3 4 5 6 7 8 9 10 #recent calls Power

104. Gogolook Confidential Power of SVM for binary classification ★Power of SVM for binary classification 0.8 0.85 0.9 0.95 1.0 3 4 5 6 7 8 9 10 #recent calls Power

105. Gogolook Confidential What is type I error rate? ★What is type I error rate? Type I error: The probability of misclassify a class B sampleto class A. 性別 Classifier 5% this is a male

106. Gogolook Confidential What is type I error rate? ★What is type I error rate? Type I error: The probability of misclassify a class B sampleto class A. 性別 Classifier 5% this is a male

107. Gogolook Confidential Type I error comparison ★Type I error comparison 0 0.05 0.1 0.15 0.3 3 4 5 6 7 8 9 10 #recent calls Type I error 0.2 0.25

108. Gogolook Confidential 這點小成果讓我稍稍放鬆地去逛街，突然電話響一聲，我開心地接了起來…

109. Gogolook Confidential 結果，對方掛斷了

110. Gogolook Confidential 響一聲掛斷的惡意電話 ★響一聲掛斷的惡意電話  “響一聲掛斷”(one-ring call) 是一種引誘接電話者回撥的惡意電話，通常伴隨著高額付款電話。  於是我們先觀察“響一聲掛斷”這類型電話號碼的call patterns。

111. Gogolook Confidential Call patterns of one-ring calls ★Call patterns of one-ring calls Numbers Mean duration of ringing(seconds) Mean duration ofout calls (seconds) 0982-415-XXX 1.6 0 0982-420-XXX 3.6 0 0982-495-XXX 5.2 1.25 04-3-704-XXXX 0.9 0 0923-931-XXX 6.7 2.6

112. Gogolook Confidential Feature comparison Ratio of new opponents Fraud Marketing Normal One-ring 0 0.2 0.4 0.6 0.8

113. Gogolook Confidential Feature comparison Ratio of in calls 0 0.1 0.2 0.3 0.4 0.5 Fraud Marketing Normal One-ring

114. Gogolook Confidential Feature comparison Ratio of missed calls 0 0.2 0.4 0.6 0.8 Fraud Marketing Normal One-ring

115. Gogolook Confidential ★Naïve method Similarly, without machine learning we can design rules such as:

116. Gogolook Confidential ★Naïve method Similarly, without machine learning we can design rules such as: Rule1: The mean of the ringing duration is less then 7 seconds. and Rule 2: The mean of the outcall duration is less than 3 seconds. Then we claim that it is a one-ring spam call.

117. Gogolook Confidential ★Problems 1. Too many features… 2. How to determine the rule? 3. New observations.

118. Gogolook Confidential ★Problem 3 Numbers Mean duration of ringing(seconds) Mean duration ofout calls (seconds) 0982-415-XXX 1.6 0 0982-420-XXX 3.6 0 0982-495-XXX 5.2 1.25 04-3-704-XXXX 0.9 0 0923-931-XXX 6.7 2.6

119. Gogolook Confidential Numbers Mean duration of ringing(seconds) Mean duration ofout calls (seconds) 0982-415-XXX 1.6 0 0982-420-XXX 3.6 0 0982-495-XXX 5.2 1.25 04-3-704-XXXX 0.9 0 0923-931-XXX 6.7 2.6 04-2-676-XXXX 15.7 1.4 ★Problem 3 New observation

120. Gogolook Confidential Numbers Mean duration of ringing(seconds) Mean duration ofout calls (seconds) 0982-415-XXX 1.6 0 0982-420-XXX 3.6 0 0982-495-XXX 5.2 1.25 04-3-704-XXXX 0.9 0 0923-931-XXX 6.7 2.6 04-2-676-XXXX 15.7 (S.D.=10.7) 1.4 ★Problem 3

121. Gogolook Confidential Machine learning can efficiently “learn” from new data and create rules for us.

122. Gogolook Confidential Power of SVM for multi-classification ★Power of SVM for multi-classification 0.8 0.85 0.9 0.95 1.0 3 4 5 6 7 8 9 10 #recent calls Power

123. Gogolook Confidential Accuracy comparison ★Accuracy comparison 3 4 5 6 7 8 9 10 #recent calls 0 0.05 0.1 0.15 0.3 0.2 0.25 Type I error

124. Gogolook Confidential Deployment All the algorithms have been implemented in the whoscallapp, so how does it work?

125. Gogolook Confidential OO推銷小明 Data center Classifier calculating… 0984-003-XXX 回傳:此號碼可能為推銷電話所需時間: 50-100 milliseconds

126. Gogolook Confidential What’s next?

127. Gogolook Confidential Improvements of the classification model 1. Fraud numbers analysis 2. Fuzzy classification algorithm 3. Spam-category scores 4. Cooperate with more solid outside sources 5. Generalize to other countries. Much more… ★Improvements of the classification model

128. Gogolook Confidential Future perspectives 1. User’s tag correction mechanisms 2. Personalized penalty setting 3. Anti-countermeasures 4. Extend to SMS spam detection 5. Clustering vs. user tags 6. Spam detect Scam detection ★Future perspectives

129. Gogolook Confidential Creating a contact network of trust 感謝大家寶貴的時間

資料科學在 Whoscall 產品體系中的角色

台灣資料科學年會

資料科學在 Whoscall 產品體系中的角色