1.
WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2.
How data is transforming
the Dutch media industry
Maurits van der Goes | RTL Netherlands
#UnifiedDataAnalytics #SparkAISummit

3.
4

4.
RTL NL
5
8.9 million
Daily TV viewers
LINEAIR TELEVISION
779 million
Online views per month
ONLINE VIDEO
2.3 million
unique vistors daily
DIGITAL PUBLISHING

5.
6
Consumers Content
What
When
Where

6.
7

7.
8

8.
9
Personalisation Emotion
Detection
Talkshow
Analysis
Automated
Trailers
Ratings
Forecasting

9.
10
Personalisation

10.
11
Domain News
Content Articles
Model Content

11.
Taxonomy SIMILARITY TF-IDF
Embeddings
23.3%
TF.IDF
22.2%
Taxonomy
17.6%
Editor’s pick
(baseline)
Random
-19.3%
Uplift
14

12.
15
Domain Films & Series
Content Long Video
Model Behavior

13.
16
Explainability Neural networks Last watched A/B testing

14.
17
30 minutes more
VIEWING TIME
per user per month

15.
18
Emotion
Detection

16.
19
We tell stories
that touch the
mind & heart
MEDIA =
EMOTION

17.
Emotion detection
20
Face& Emotion
Detection
Musical Genre &
Mood
Speaker Emotion

18.
21
oarriaga/face_classification

19.
BERT
22
Bidirectional Encoder Representations from Transformers (Devlin et al., 2018)
google-research/bert
(Kaggle)
BERT-Base, Multilingual Cased
• 104 languages
• 12-layer
• 768-hidden
• 12-heads
• 110M parameters

20.
23

21.
24
Talkshow
Analysis

22.
FuzzyWuzzy
Levenshtein Distance
Scenario & subtitles matching
25
SubtitlesScenario
Items + TS

23.
Item classification
26
MLlib
• Crime
• Entertainment
• Lifestyle
• Royalty
NaiveBayes 0,89946
Logistic Regression
(Count Vector Features)
0,84533
Logistic Regression
(TF-IDF Features)
0,81268
RandomForrest 0,56564
Item

24.
27

25.
28
Ratings
Forecasting

26.
Model components
–0.12 pp/yr
–2.65 pp/yr
–1.95 pp/yr
Four weather variables (temperature, wind, precipitation,
sunshine), measured with respect to average conditions
for the time of year
Interactions:
• n = 1, 2 Fourier terms
• saturday/sunday
• internal
𝑓 𝑡 = $
%&'
(
𝑎% cos
2π𝑛𝑡
𝑃
+ 𝑏% sin
2π𝑛𝑡
𝑃
, 𝑃 = 1 year
Truncated Fourier series:Dummy variables for weekdays
Interaction with n = 1, 2 Fourier terms
Piecewise-linear functionDummy variables for various “events” (e.g. holidays,
various sports events)
Components: trendComponents: trend + seasonalComponents: trend + seasonal + weekdayComponents: trend + seasonal + weekday + weatherComponents: trend + seasonal + weekday + weather
+ event
29

27.
Overall Statistics
Manual Data Reduction
Mean Error 0.93 0.29 -68%
Mean
Percentage
Error
5.4% 1.3% -77%
Mean
Absolute
Error
1.63 1.27 -22%
Mean
Absolute
Percentage
Error
9.5% 7.3% -23%
30

28.
31
Automated
Trailers

29.
Why PROMO GENERATION?
Promos are ideal for building awareness, branding and teasing the
audience.
Decline in viewership per channel -> increase in channels &
promos.
Promo generation is costly, a highly labor-intensive process.
Artificial Intelligence can aid in analyzing, selecting an combining
potential candidate shots and scenes.
32

30.
Video
Aesthetics
Optical Flow
Shot
Segmentation
Key Frame
Extraction
Object Detection,
Recognition &
Segmentation
Face Detection,
Recognition &
Emotion
Visual Similarity
Feature extraction
(Pachyderm)
33
Features

31.
MAN OR MACHINE?

32.
Man or Machine?MAN OR MACHINE?

33.
MAN OR MACHINE?

34.
37
What WhenWhere
Personalisation Emotion
Detection
Talkshow
Analysis
Automated
Trailers
& moreRatings
Forecasting

35.
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT