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資料科學在 Whoscall 產品體系中的角色
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Gogolook Confidential
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22-Nov-2014 -
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郭建甫 (Jeff Kuo) Gogolook 走著瞧公司創辦人兼執行長 郭博士與鄭勝丰、宋政桓一同創立走著瞧 (Gogolook) 公司，目前擔任 WhosCall 開發團隊執行長。曾就讀於成大工業設計學系，畢業於清華大學工業工程研究所，其專精領域為產品設計與使用者經驗研究。專業經歷為德國 Heinz Nixdorf Institute 研究員、先構技研(股)公司共同創辦人、安通國際(股)公司新事業發展總監等。 --- 高義銘 (Yimin Kao) Gogolook 走著瞧公司資料科學家 目前為走著瞧(Gogolook)公司數據分析科學家，畢業於美國北卡州立大學統計系。專業研究領域包含統計分類與分群方法、貝氏模型和空間統計。應用於電腦病毒封包偵測、基因關聯檢測和預測颶風路徑等。以散播音樂和歡樂為人生志向。
Transcript of 資料科學在 Whoscall 產品體系中的角色
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
Key Features
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★ Instant Caller Identification
LINE whoscall identifies 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
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★ Database with over 600 Million Phone Numbers
LINE whoscall boasts an online database with over 600 million phone numbers. The database of LINE whoscall covers yellow pages, spammers, telemarketers, costumer services...,etc. with numerous community tags contributed by users and comments based on real users’ experiences.
Database & Number Details
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Incoming Call Dialogue
Incoming Call DialogueFraud 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’s service. LINE whoscalluser can tag a phone 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
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★ World’s Largest Yellow Page Database
★ Offline Database Available for Free
LINE whoscall owns one of the world’s largest onlinephone number 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 whoscall
every month.
3000 spammer numbers
are reported by LINE whoscalluser every day.
Number Identification
– 2014.07 – 2014.07
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Market
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Honors
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What we will be…
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Vision
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資料科學在 whoscall 的應用GOGOLOOK 資料科學家 高義銘
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★ 日常生活經常會遇到的問題
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★ 人面對未知的事物就會有一種…
我有一種不祥的預感!
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★ 坊間流傳著許多解決此問題的 APPs
小熊來電通知
Presenter
Presentation Notes
老闆說這張圖怪怪的….他也說不上來…
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★ 坊間流傳著許多解決此問題的 APPs
小熊來電通知
Presenter
Presentation Notes
改成這樣之後…他就說嗯….好像對了
Gogolook Confidential
★ Why whoscall?
因為… 他是連 Google 執行長都說讚的軟體!
唉呦，讚喔
Presenter
Presentation Notes
甚麼事都可以不相信，但就是不能不相信google
Gogolook Confidential
whoscall 是如何解決未知來電的問題咧?
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★ Technologies adopted
1. Yellow pages:HiPage, Yelp, Zenrin…
2. Google search
3. Other sources
Technologies adopted
Presenter
Presentation Notes
Other source..
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★ Technologies adopted
Technologies adopted
4. 使用者回報與標記
Presenter
Presentation Notes
Crowdsourcing
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★ Technologies adopted
Technologies adopted
4. 使用者回報與標記
Presenter
Presentation Notes
Crowdsourcing, 也可以想像成一種電話號碼界中的wikipedia….
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?
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★ Problem we want to solve
For an unknown phone number:• No google result• No user tag / report• Not a whoscall user
Problem we want to solve
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★ Problem we want to solve
For an unknown phone number:• No google result• No user tag / report• Not a whoscall user
Can we determine if it’s a spam number?
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 whoscall user
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 whoscall user
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 whoscall user
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 whoscall user
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!!)
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★ Scenario
Scenario
OO推銷
小明
小明妹
小明哥
?
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★ We think it should work because…
whoscall userbase ( = potential sensors)• > 10 million installations• > 10 thousands tags (daily)• > 30 million phone calls (daily)
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Analysis procedures
Analysis procedures1. Collect call logs2. Compare with user tags3. Explore call behaviors4. Extract features5. Classify unknown numbers using
machine learning techniques
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★ Collect call logs
• Recruit a group of voluntary whoscall users as our sensors.
• Collect phone call logs from these sensors for a month.
Collect call logs
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★ 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
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Analysis procedures
Analysis procedures1. Collect call logs2. Compare with user tags3. Explore call behaviors4. Extract features5. 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
Presenter
Presentation Notes
A picture worth a thousand words
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★ Compare with user tags
• Compare these phone numbers with user reports from whoscall database (封鎖記錄)
Compare with user tags
Normal numbers
0987-991-XXX0986-225-XXX02-2675-XXXX03-862-XXXX
...
02-2543-XXXX03-556-XXXX886-XXXX
…
推銷電話
02-2783-XXXX886-903-XXXX0800-000-XXX
…
惡意電話
Presenter
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All the phone numbers are manipulated ……
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★ 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: 7854Normal: 4000Spam: 3854
Gogolook Confidential
Analysis procedures
Analysis procedures1. Collect call logs2. Compare with user tags3. Explore call behaviors4. Extract features5. Classify unknown numbers using
machine learning techniques
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Normal numbers
0
5
10
15
20 Calls =195 (in 66, out 129)Opponents = 72 (in 21, out 58)
★ Normal numbers
Presenter
Presentation Notes
周末附近就會有一個peak
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★ Spam numbersSpam 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 procedures1. Collect call logs2. Compare with user tags3. Explore call behaviors4. Extract features5. 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.
Presenter
Presentation Notes
可量測的一個觀察到的現象
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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
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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
Presenter
Presentation Notes
但有些feature其實不容易量化，像是…
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Example
Or, we want to analyze a company, we might look at features:
公司向心力
★ Example
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Example
Or, we want to analyze a company, we might look at features:
CEO 帥氣程度
★ Example
Presenter
Presentation Notes
Extracting features need experience, creativity and insight.
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Features for call patterns
Ratio of out calls
0.8
0.6
0.4
0.2
0.0Fraud Marketing Normal
Presenter
Presentation Notes
這些電話號碼打給別人的比例
Gogolook Confidential
Features for call patterns
Ratio of recurring opponents
Fraud Marketing Normal0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Presenter
Presentation Notes
和這個電話號碼有兩次以上交流的比例
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
Presenter
Presentation Notes
此號碼打出去而沒有人接得比例
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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
Presenter
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在 working time 打電話的比例
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Features for call patterns
Median of call durations
Fraud Marketing Normal
50
40
30
20
10
0
60
seconds
Presenter
Presentation Notes
通話長度的中位數
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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
Presenter
Presentation Notes
In total we have approximately 64 features.
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Analysis procedures
Analysis procedures1. Collect call logs2. Compare with user tags3. Explore call behaviors4. Extract features5. 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
Presenter
Presentation Notes
Then we claim
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★ Problem 1
Too many features…
Presenter
Presentation Notes
Then we claim
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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。
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Machine learning techniques 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
Presenter
Presentation Notes
We first do binary classification, which means we only classify unknown numbers as spam or non-spam Why we use SVM, simple, easy, fast, and efficient.
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Support vector machine for binary classification
★Support vector machine for binary classification
Presenter
Presentation Notes
We first do binary classification, which means we only classify unknown numbers as spam or non-spam Why we use SVM, simple, easy, fast, and efficient.
Gogolook Confidential
Support vector machine for binary classification
★Support vector machine for binary classification
Presenter
Presentation Notes
We first do binary classification, which means we only classify unknown numbers as spam or non-spam Why we use SVM, simple, easy, fast, and efficient.
Gogolook Confidential
Support vector machine for binary classification
★Support vector machine for binary classification
Presenter
Presentation Notes
We first do binary classification, which means we only classify unknown numbers as spam or non-spam Why we use SVM, simple, easy, fast, and efficient.
Gogolook Confidential
Support vector machine for binary classification
★Support vector machine for binary classification
Presenter
Presentation Notes
We first do binary classification, which means we only classify unknown numbers as spam or non-spam Why we use SVM, simple, easy, fast, and efficient.
Gogolook Confidential
Support vector machine for binary classification
★Support vector machine for binary classification
Presenter
Presentation Notes
We first do binary classification, which means we only classify unknown numbers as spam or non-spam Why we use SVM, simple, easy, fast, and efficient.
Gogolook Confidential
Support vector machine for binary classification
★Support vector machine for binary classification
Presenter
Presentation Notes
We first do binary classification, which means we only classify unknown numbers as spam or non-spam Why we use SVM, simple, easy, fast, and efficient.
Gogolook Confidential
Support vector machine for binary classification
★Support vector machine for binary classification
Presenter
Presentation Notes
We first do binary classification, which means we only classify unknown numbers as spam or non-spam Why we use SVM, simple, easy, fast, and efficient.
Gogolook Confidential
這樣就夠了嗎？
Presenter
Presentation Notes
Data 介紹玩了，Machine learning technique 也決定了。 ? 要改
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Real-life scenario
★ Real-life scenario
When will we require a spam number prediction?Ans: The time a phone call reaches a whoscall user
We want to predict whether a number is spam as EARLY as possible in order to prevent further victims…
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Real-life scenario
Time
# recent calls
Victim 1 Victim 2 Victim 3
XX推銷★ Real-life scenario
推銷電話
Presenter
Presentation Notes
饅頭人
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Let’s look at the performances of SVM under different numbers of recent calls
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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?
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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.
Presenter
Presentation Notes
possible to visualize, stable parameter estimation, 減少所謂 collinearity
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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…
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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
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Feature selection results
★ Feature selection results
10 15 20 25 30
3 recent calls
5 recent calls
10 recent calls0.8
0.85
0.9
0.95
1.0
# features
Accuracy
Presenter
Presentation Notes
這個圖展現了feature selection 的結果， X 軸是選取feature的個數 Y軸是準確率。 第一 當feature數越多，準確率越高。 三條線分別代表不同的observation windows 舉例來說，黃色這條線代表如果我只看這個未知號碼的前兩通call，橘色就是前四通… Observation window 越長，你其實不需要很多feature就可以達到一定的accuracy. 譬如紅色這條線表示你看了前九通的call log, 原則上你只需要 大概10-15個feature左右，你的accuracy 其實就已經趨於穩定了!!! 在這裡我決定取20 features, 因為可以看到20個feature 之後，不同的observation windows 也都趨於穩定。
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Feature selection results
★ Feature selection results
10 15 20 25 30
3 recent calls
5 recent calls
10 recent calls0.8
0.85
0.9
0.95
1.0
# features
Accuracy
Presenter
Presentation Notes
這個圖大致上展現了feature selection 的結果， X 軸是選取feature的個數 Y軸是準確率。 第一 當feature數越多，準確率越高。 三條線分別代表不同的observation windows 舉例來說，黃色這條線代表如果我只看這個未知號碼的前兩通call，橘色就是前四通… Observation window 越長，你其實不需要很多feature就可以達到一定的accuracy. 譬如紅色這條線表示你看了前九通的call log, 原則上你只需要 大概10-15個feature左右，你的accuracy 其實就已經趨於穩定了!!! 在這裡我決定取20 features, 因為可以看到20個feature 之後，不同的observation windows 也都趨於穩定。
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Feature selection results
★ Feature selection results
10 15 20 25 30
3 recent calls
5 recent calls
10 recent calls0.8
0.85
0.9
0.95
1.0
# features
Accuracy
Presenter
Presentation Notes
這個圖大致上展現了feature selection 的結果， X 軸是選取feature的個數 Y軸是準確率。 第一 當feature數越多，準確率越高。 三條線分別代表不同的observation windows 舉例來說，黃色這條線代表如果我只看這個未知號碼的前兩通call，橘色就是前四通… Observation window 越長，你其實不需要很多feature就可以達到一定的accuracy. 譬如紅色這條線表示你看了前九通的call log, 原則上你只需要 大概10-15個feature左右，你的accuracy 其實就已經趨於穩定了!!! 在這裡我決定取20 features, 因為可以看到20個feature 之後，不同的observation windows 也都趨於穩定。
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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
Presenter
Presentation Notes
54 20
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★ 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
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Done?
阿不就好棒棒?
Presenter
Presentation Notes
有一天我就是很好奇想看一下各種type of 的spam表現得如何!!於是我看了所謂的Power
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What is power?
★ What is power?
Power of class A: The probability of accurately classify a class A sample to class A.
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What is power?
★ What is power?
Power of class A: The probability of accurately classify a class A sample to class A.
性別Classifier
97.5% this is a male
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What is power?
★ What is power?
Power of class A: The probability of accurately classify a class A sample to class A.
性別Classifier
97.5% this is a male
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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
Presenter
Presentation Notes
挖…….Fraud number 的power 好像沒有想像中的理想耶….糟糕!!!! 拿掉Accuracy!!!
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義銘，加油好嗎?
Presenter
Presentation Notes
所以我重新回去看了Data 的分布
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★ 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: 7854Normal: 4000Spam: 3854
Presenter
Presentation Notes
Imbalanced samples problem
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★ 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: 7854Normal: 4000Spam: 3854
Presenter
Presentation Notes
Imbalanced samples problem
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Marketing numbers vs. normal numbers
★ Marketing numbers vs. normal numbers
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# recent calls
Accuracy
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Fraud numbers vs. normal numbers
★ Fraud numbers vs. normal numbers
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Accuracy
Presenter
Presentation Notes
Fraud 雖然比Marketing number困難一點，但還是有不錯的能力..
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一種摻在一起做撒尿牛丸的概念…
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Power of SVM for multi-classification
★ Power of SVM for multi-classification
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# recent calls
Power
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Power of SVM for binary classification
★ Power of SVM for binary classification
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# recent calls
Power
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What is type I error rate?
★ What is type I error rate?
Type I error: The probability of misclassify a class B sample to class A.
性別Classifier
5% this is a male
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What is type I error rate?
★ What is type I error rate?
Type I error: The probability of misclassify a class B sample to class A.
性別Classifier
5% this is a male
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Type I error comparison
★ Type I error comparison
0
0.05
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0.15
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# recent calls
Type I error0.2
0.25
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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…
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結果，對方掛斷了
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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。
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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
Presenter
Presentation Notes
同樣的…..我們可以extract features from call patterns 來看一下one-ring, fraud, marketing, and normal number 的不同
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Feature comparison
Ratio of new opponents
Fraud Marketing NormalOne-ring0
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Feature comparison
Ratio of in calls
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Fraud Marketing NormalOne-ring
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Feature comparison
Ratio of missed calls
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Fraud Marketing NormalOne-ring
Presenter
Presentation Notes
來看一些不一樣的
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★ Naïve method
Similarly, without machine learning we can design rules such as:
Presenter
Presentation Notes
但同樣的問題，這個rule是你訂的…有可能會發生以下的事情!!
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★ 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.
Presenter
Presentation Notes
但同樣的問題，這個rule是你訂的…有可能會發生以下的事情!!
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★ Problems
1. Too many features…2. How to determine the rule?3. New observations.
Presenter
Presentation Notes
Then we claim
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★ 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
Presenter
Presentation Notes
依照上面你訂的rule….這個號碼將會被判斷成不是one-ring….但他真的不是嗎??
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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
Presenter
Presentation Notes
依照上面你訂的rule….這個號碼將會被判斷成不是one-ring….但他真的不是嗎??
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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
Presenter
Presentation Notes
依照上面你訂的rule….這個號碼將會被判斷成不是one-ring….但他真的不是嗎??
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Machine learning can efficiently “learn” from new data and create rules for us.
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Power of SVM for multi-classification
★ Power of SVM for multi-classification
0.8
0.85
0.9
0.95
1.0
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# recent calls
Power
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Accuracy comparison
★ Accuracy comparison
3 4 5 6 7 8 9 10
# recent calls
0
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Type I error
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DeploymentAll the algorithms have been implemented in the whoscall app, so how does it work?
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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
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What’s next?
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Improvements of the classification model
1. Fraud numbers analysis2. Fuzzy classification algorithm3. Spam-category scores4. Cooperate with more solid outside sources5. Generalize to other countries.
Much more…
★ Improvements of the classification model
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Future perspectives
1. User’s tag correction mechanisms2. Personalized penalty setting3. Anti-countermeasures4. Extend to SMS spam detection5. Clustering vs. user tags6. Spam detect Scam detection
★ Future perspectives
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Creating a contact network of trust
感謝大家寶貴的時間
