Only to my friends

1. Big Data Consultant &
Data Scientist
趙國仁
Craig Chao
chaocraig@gmail.com
從行動廣告大數據觀點談 Big Data
“BIG DATA” from Mobile Ads Perspective

2. Prelog – Myths of Big Data
• Big Data, Big Hype?

3. Prelog – Myths of Big Data
• Machine Learning & Statistics have been
used in many places, nothing new in Big
Data?

4. Prelog – Myths of Big Data
• Big Data is Hadoop / Open Source?

5. “BIG” data?

6. big “DATA”?

7. Challenges of Big Data - 4V
資料量大 資料多樣性
資料輸入
和處理速度快
資料真實性

8. 4V & Solution Directions
Challenges Directions
Volume Scalability
Velocity Real-time Response
Variety All data
Veracity Knowledge
engineering &
Machine intelligence

9. The Revolution of Big Data
DATA
Hypotheses
Statistical Analysis
BIG DATA
Hypotheses
Machine Learning
Data Mining
Machine-generated
Sampling, Multi-variant… All, Hyper space, …
Volume, Velocity, Variety, Veracity
Human-explainable

10. Reports vs. models
Segments Reports
For Human
(Explanatory)
Models Data-driven
Actions
Efficiency Intelligence Effectiveness
Data Science is the art of turning data into actions.

11. Data Science Venn Diagram
Stat vs. MLDM?
(Big) Programming?
Tags?

13. Common Data Categories
• Persona
– Age, Gender, Birth date, City,
…
• Attributes
– Phone brand/model, location,
time, App, browser, banner…
• Behavior
– Click, Conversion(Installation,
Cart, Purchase, …),
Activation, Payment…

15. 大數據分析找到更多潛在客群
指定
投遞
指定
排除
曝光
頻次
APP
偵測
投放
情境
投放
廣告
偏好
指定
品牌
粉絲
產品
使用者
興趣
偏好
收集用戶行為數據
Targeting Capability
11
22
33
大數據分析
找到潛在客群
優化投放

16. In-database Processing(MPP)

17. Computation Performance
Source: Matei Zaharia(2013)

18. Pricing Engine
Framework
Kafka
HDFSHDFS
Apache
Spark
Apache
Spark
Jenkins
Realtime processors
( Spark Streaming)
Realtime processors
( Spark Streaming)
DataInjection
Speed Layer
Batch Layer
ServingLayer
Kafka
DataStreaming
CouchbaseCouchbase
Docker Container
Avro
Avro
Akka/Scala Actors

19. 4R: Reach, Richness,
Representation, Range
Reach
Richness
High
High
Low
使用者接觸量 (DAU)
資料豐富度
(Behavioral data)
Range
High
系統範圍
( Affiliate of
whole context)
Representation
呈現形式與內容
(Format &
Content)

20. Data Economy
Traditional -> Internet Economy
HighREACH
RICHNESS
High
Low
Traditional
Economy
Internet Economy
(quality)
(quantity)

21. Reach: The Value Funnel
CPM campaign:
Revenue = N/1000 ⋅CPM
CPC campaign:
Revenue = N ⋅ CTR ⋅ CPC
CPA campaign:
Revenue = N ⋅ CTR ⋅
CVR⋅ CPA
UU Reach (DAU)
ARPU = Life-time Value

22. Richness
Data Quality  Predictive Power

23. Richness: Predictive Power
APP 類型偏好
使用裝置
使用時間
定位區域
廣告行為偏好
Conversions Logs
Behavioral Data Attribution Data

24. Richness
• Data Quality Richness
• Data Utilization Richness
– Call taxi (short vs. long route)
– Download times vs. Activation days
• Data Model Richness

25. Simplest Model

26. Logistic Regression

27. LM & LR
Source: http://www.saedsayad.com/logistic_regression.htm
歸一化的好處在於數值具備可比性和收斂的邊界
Likelihood

28. User-based / Item-based
Recommendation

29. Matrix = Associations
Rose Navy Olive
Alice 0 +4 0
Bob 0 0 +2
Carol -1 0 -2
Dave +3 0 0
• Things are associated
Like people to colors
• Associations have
strengths
Like preferences and
dislikes
• Can quantify associations
Alice loves navy = +4,
Carol dislikes olive = -2
• We don’t know all
associations
Many implicit zeroes
Source: Sean Owen(2012), Cloudera

30. In Terms of Few Features
• Can explain associations by appealing to underlying features in
common (e.g. “blue-ness”)
• Relatively few (one “blue-ness”, but many shades)
(Alice)
(Blue)
(Navy)
Source: Sean Owen(2012), Cloudera

31. Losing Information is Helpful
• When k (= features) is small, information is lost
• Factorization is approximate
(Alice appears to like blue-ish periwinkle too)
(Alice)
(Blue)
(Navy)
(Periwinkle)
Source: Sean Owen(2012), Cloudera

32. Singular Value Decomposition
AA m
=
n
SS
k
k• T’T’
n
m
•ΣΣ

33. Context-aware Matrix
Factorization

34. Sample FM Matrix

35. Optimization Perspective

36. Gradient Descent

37. FM with SGD
Rendle, S.(2012)

38. Factorization Machine - R
• #
• # Factorization machine
• #
• logis <- function(x) {
• result <- 1./(1+exp(-x))
• return(result)
• }
• wTx <- function(x, w, V) { #decision value
• V.size <- dim(V)
• p <- V.size[1] #rows
• k <- V.size[2] #columns
• tmp = 0;
• for (i in 1:k) {
• tmp1 <- 0;
• tmp2 <- 0;
• for (j in 1:p) {
• tmp1 = tmp1 + V[j,i] %*% x[j];
• tmp2 = tmp2 + (V[j,i] %*% x[j])^2;
• }
• tmp = tmp + (tmp1^2-tmp2);
• }
• tmp = 0.5*tmp;
• x[length(x)+1] <- 1
• result <- x %*% t(w) + tmp #x is all features + bias
• return(result)
• }
Un-optimized version

39. Factorization Machine - R
• FMlogistic_<- function(A, y, At, yt, k, lambda, eta, numiter) {
• #
• # A: input matrix
• # y: lable
• # At: Test of A
• # yt: Test of y
• # k: number of latent factors
• # lambda: regularization parameters
• # eta: learning rate
• # numiter: number of interactions
• #
•
• A.size <- dim(A) #[numinst, numfeat]
• numinst <- A.size[1]
• numfeat <- A.size[2]
• nt <- numinst
•
• #B <- matrix(1, numinst, numfeat)
• #B.size <- dim(B)
• #Bt <- matrix(1, B.size[1], B.size[2])
• sigma <- 0.1 # standard deviation
• # Start here… Model parameter theda = (w0, w, V)
• w0 <- matrix(0, 1, numfeat+1) # weights of features, +1 is for bias
• w <- matrix(0, 1, numfeat+1) # weights of features, +1 is for bias
• #V0 <- matrix(c(rnorm(numfeat*k, mean = 0, sd = sigma)), numfeat, k) # generates an numfeat-by-k output matrix
• V0 <- matrix(0.1, numfeat, k)
• V <- matrix(0, numfeat, k) # output matrix

40. Factorization Machine - R
• for (iter in 1:numiter) {
• for (i in 1:numinst) {
• for (j in 1:numfeat) {
• w[j] <- w0[j] - eta*((logis(wTx(A[i,], w0, V0) %*% y[i])-1)*y[i]*A[i,j]+2*lambda*w0[j])
• for (numlatent in 1:k) {
• ind <- setdiff(1:numfeat, j)
• hx <- A[i,j] %*% sum( V0[ind,numlatent] * t(A[i,ind]) )
• V[j,numlatent] <- V0[j,numlatent] - eta*((logis(wTx(A[i,], w0, V0)*y[i])-1)*y[i] * hx + 2*lambda*V0[j,numlatent])
• }
• }
• w[length(w)] = w0[length(w0)] - eta*((logis(wTx(A[i,], w0, V0)*y[i])-1)*y[i]+2*lambda*w0[length(w0)])
• V0 <- V
• w0 <- w
• }
• yhat <- matrix(0, nt, 1)
• for (i in 1:nt) {
• yhat[i] <- wTx(At[i,], w, V)
• }
• prob <- 1./(1+exp(-yhat));
• yhat[yhat>=0] <- 1;
• yhat[yhat <0] <- -1;
• acc <- sum(yt==yhat)/nt;
• cat( sprintf('n#iter = %d, training accurcy = %fn', iter , acc) )
•
• }
• return( list(prob, yhat) )
• }

41. Richness
• Data Model Richness

42. Representation

43. Representation

44. Representation
TV campaign
Range
Mobile Campaign Offline Campaign
Reach
Richness
Cross-screen Effect

45. 成功案例：掌握 4R 成效更優異
！
Cross-screen synergy
Big data synergy with Cross-screen effect
+TV

46. Range
- Roger Martin
Rothman School of Management, Toronto
If only attach importance to quantify the business
model, it will not have the ability to find a potential
growth opportunities: "The pursuit of quantifying
the biggest problem is that people ignore the
context of the behavior generated, detached from
the context of the event, and have not been
included in the model ignores variables
effectiveness. "
企業若只重視量化模式，
將無法擁有尋得潛在成長
契機的能力：「追求量化
最大的問題在於，忽略
人們產生行為的脈絡，
把事件從情境中抽離，
且忽略沒有被納入模式
中的變數效力。」

47. Range

48. Range
• Google trend, Viral install…

49. High
4R: Reach, Richness,
Representation, Range
Reach
Richness
High
High
Low 使用者接觸量 (DAU)
資料豐富度
(Behavioral data)
Range
系統範圍
( Affiliate of
whole context)
Representation
呈現形式與內容
(Format &
Content)

50. Big Data - Google Now

51. Dulingo

52. Facebook Personal Assistant

53. 全球最先進
的追蹤器：
活動追蹤、
睡眠追蹤、
Smart
Coach 和
心臟健康記
錄
iPaaS 幫助各公司在雲端中及內部部
署連接企業應用程式
癌
症
分
析
視
覺
化

54. iPod
之父
Tony
Fadell
創建的
恆溫器
智慧家
居公司
醫療資
料的整
合與分
析
政
府
支
出
公
開
平
台
開
車
更
省
油
、
安
全
服務科
技領域
人士的
在線理
財咨詢
管理平
台

55. World, Model & Theory
Credit: John F. Sowa

57. BIG
DAT
A
資料始終為了人性
Use Data, not be Used.

58. Summary - Innovation

59. 謝謝大家！
chaocraig@gmail.com

60. Data Scientist as CEO of Data
Source: 經理人 (117)