Consumer behavior shift rapidly in recent years and deliver the most relevant content and products to the right person at the right time & place is the crucial factor for company to stay competitive. This session we will understand how data science & natural language processing (NLP) used to analyze data to personalize and suggest relevant content or products, effectively and elevating the customer experience

1. Data science & natural language processing
(NLP) for content recommendation &
personalized experience
Kim Ming
Media Prima Digital

2. WHO ARE YOUR USERS?
WHAT DO THEY REALLY LOOK
FOR?

3. Overview
1. Business goal 2. Data & technology 3. Data science & NLP 4. Impact

4. Understand the business:

5. The Problems:
● Understand 10+ million users behavior & better serve them

6. User behavior shift

7. Customer journey & decision

8. Personalized user experience

9. Data & Technology
● capture, store, process & create data-driven application

10. Data pipelines
Enteprise
Datawarehouse
Modeling |
Analytic Layer
Main Source
Platform | API
Source Systems
Data Prep &
ETL Layer
Presentation
| Serving Layer

11. User, content & interaction metadata
User metadata content metadata User interaction metadata

12. Data Science & NLP
● Understand user behavior better, serve them relevant content

13. User profiling & personalized content & ads
User behavior & interaction User behavior profile and segment Personalized content & ads

14. Audience profile & segment
Music
Celebrities
Entertainment Seekers
Entertainment
English Literate
Malay Literate
Home Improvement
NST Users
Harian Metro Users
Berita Harian Users
Travel
Automobile
Tonton monthly subs – last visit 20 days
And many more… total 400+ audience profiles

16. Same sentence, people say it differently :
● “I want a fresh apple”
● “Where can I find a fresh apple in this city to eat”
● “Ooi, give me apple la ….”

17. Languages are ambiguous
● “I love Blackberry”.

18. Existing solution provider & cloud not support Malay
language (yet) 

20. Build Malay text processing & NLP library
stemming stopwords Word segment Part-of-speech
Name entity
recognition
Abbrevation Ambigious term Word similarity Translation

21. Automate Document classification & Brand-Safe score

22. Automate Document classification & Brand-Safe score (2)
Predicted categories: bisnes/auto
Entities: Mazda (with Negative score = 0 )
Comment: it’s safe for Mazda or automobile
manufacturer
Predicted categories: berita/kes
Entities: Mazda (with Negative score = 0. 80 )
Comment: it’s not safe for Mazda or automobile
manufacturer

23. Build local data dictionary
600+ content categories
1.1 millions
entities/document
English & Malay terms

24. Build local knowledge graph

25. Local word embedding & word similarity

26. Local word embedding & word similarity (2)

27. NSTP article recommendation

28. Tonton video recommendation (2)

29. Impact & Benefit
● Higher user engagement, better campaign performance
● Automate manual work such as document classification & label
● Dynamic semantic publishing

30. +20% users +30% Page-views +25% user avg time spent
User profiling & content recommendation

31. 50000+ documents/contents auto-tagged
50+ man-hours saved per week
Improved content categorization
Improved campaign performance
NLP & automate content classification

32. Questions?

33. Thank You!
We’re hiring!
Kim.meng@mediaprima.com.my
Lutherteh.github.io