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Gogolook Confidential
Gogolook Confidential 
How Started?
Gogolook Confidential 
How Started?
Gogolook Confidential 
How Started?
Gogolook Confidential 
The Best App 
For 
identifying and blocking calls 
The Best App –LINE whoscall
Gogolook Confidential
Gogolook Confidential 
KeyFeatures
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
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
Gogolook Confidential 
Incoming Call Dialogue 
Incoming Call Dialogue 
Fraud Call 
Business 
Corporation 
Restaurant
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★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
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
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
Gogolook Confidential 
Market
Gogolook Confidential 
Honors
Gogolook Confidential 
What we will be…
Gogolook Confidential 
Vision
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資料科學在whoscall的應用 
GOGOLOOK資料科學家高義銘
Gogolook Confidential 
★日常生活經常會遇到的問題
Gogolook Confidential 
★人面對未知的事物就會有一種… 
我有一種不祥的預感!
Gogolook Confidential 
★坊間流傳著許多解決此問題的APPs 
小熊來電通知
Gogolook Confidential 
★坊間流傳著許多解決此問題的APPs 
小熊來電通知
Gogolook Confidential 
★Why whoscall? 
因為…他是連Google執行長都說讚的軟體! 
唉呦,讚喔
Gogolook Confidential 
whoscall是如何解決未知來電 的問題咧?
Gogolook Confidential 
★Technologies adopted 
1. Yellow pages: 
HiPage, Yelp, 
Zenrin… 
2. Google search 
3. Other sources 
Technologies adopted
Gogolook Confidential 
★Technologies adopted 
Technologies adopted 
4. 使用者回報與標記
Gogolook Confidential 
★Technologies adopted 
Technologies adopted 
4. 使用者回報與標記
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?
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…
Gogolook Confidential 
當然不能洗洗睡,要不然我站 在這邊幹嘛?
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
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
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
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
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
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!!)
Gogolook Confidential 
★Scenario 
Scenario 
OO推銷 
小明 
小明妹 
小明哥 
?
Gogolook Confidential 
★We think it should work because… 
whoscalluserbase( = potential sensors) 
• 
> 10 million installations 
• 
> 10 thousands tags (daily) 
• 
> 30 million phone calls (daily)
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
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
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
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
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
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
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
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
Gogolook Confidential 
Normal numbers 
0 
5 
10 
15 
20 
Calls =195 (in 66, out 129) 
Opponents = 72 (in 21, out 58) 
★Normal numbers
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)
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
Gogolook Confidential 
★What is a feature? 
What is a feature? 
“Feature”is a measurable property of a phenomenon being observed.
Gogolook Confidential 
Example 
Or, we want to analyze a company, we can look at features: 
公司人數 
★Example
Gogolook Confidential 
Example 
Or, we want to analyze a company, we can look at features: 
工程師人數 
★Example
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
Gogolook Confidential 
Example 
Or, we want to analyze a company, we might look at features: 
公司向心力 
★Example
Gogolook Confidential 
Example 
Or, we want to analyze a company, we might look at features: 
CEO帥氣程度 
★Example
Gogolook Confidential 
Features for call patterns 
Ratio of out calls 
0.8 
0.6 
0.4 
0.2 
0.0 
Fraud 
Marketing 
Normal
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
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
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
Gogolook Confidential 
Features for call patterns 
Median of call durations 
Fraud 
Marketing 
Normal 
50 
40 
30 
20 
10 
0 
60 
seconds
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
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
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
Gogolook Confidential 
★Problem 1 
Too many features…
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★Problem 2 
How to determine the rule?
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Machine learning 
★Solution
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Machine learning 
★Solution 
Let the machine learn from the data
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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。
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
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Support vector machine for binary classification 
★Support vector machine for binary classification
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Support vector machine for binary classification 
★Support vector machine for binary classification
Gogolook Confidential 
Support vector machine for binary classification 
★Support vector machine for binary classification
Gogolook Confidential 
Support vector machine for binary classification 
★Support vector machine for binary classification
Gogolook Confidential 
Support vector machine for binary classification 
★Support vector machine for binary classification
Gogolook Confidential 
Support vector machine for binary classification 
★Support vector machine for binary classification
Gogolook Confidential 
Support vector machine for binary classification 
★Support vector machine for binary classification
Gogolook Confidential 
Support vector machine for binary classification 
★Support vector machine for binary classification
Gogolook Confidential 
這樣就夠了嗎?
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…
Gogolook Confidential 
Real-life scenario 
Time 
#recent calls 
Victim 1 
Victim 2 
Victim 3 
XX推銷 
★Real-life scenario 
推銷電話
Gogolook Confidential 
Let’s look at the performances of SVM under different numbers of recent calls
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
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嗯…表現的不錯,但是… 
可以再快一點嗎?
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.
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…
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
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
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
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
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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
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
Gogolook Confidential 
Done? 
阿不就好棒棒?
Gogolook Confidential 
What is power? 
★What is power? 
Power of class A: The probability of accurately classify a class A sampleto class A.
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
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
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
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義銘, 
加油好嗎?
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
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
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
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
Gogolook Confidential 
一種 
摻在一起做撒尿牛丸的概念…
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
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
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
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
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
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…
Gogolook Confidential 
結果,對方掛斷了
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。
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
Gogolook Confidential 
Feature comparison 
Ratio of new opponents 
Fraud 
Marketing 
Normal 
One-ring 
0 
0.2 
0.4 
0.6 
0.8
Gogolook Confidential 
Feature comparison 
Ratio of in calls 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
Fraud 
Marketing 
Normal 
One-ring
Gogolook Confidential 
Feature comparison 
Ratio of missed calls 
0 
0.2 
0.4 
0.6 
0.8 
Fraud 
Marketing 
Normal 
One-ring
Gogolook Confidential 
★Naïve method 
Similarly, without machine learning we can design rules such as:
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.
Gogolook Confidential 
★Problems 
1. 
Too many features… 
2. 
How to determine the rule? 
3. 
New observations.
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
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
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
Gogolook Confidential 
Machine learning can efficiently “learn” from new data and create rules for us.
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
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
Gogolook Confidential 
Deployment 
All the algorithms have been implemented in the whoscallapp, so how does it work?
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
Gogolook Confidential 
What’s next?
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
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 
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Gogolook Confidential 
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