Kim Ming from Media Prima Digital presented on using data science and natural language processing for content recommendation and personalized experiences. The goals are to understand over 10 million users' behavior better to serve them personalized content. Data is captured and stored in a data warehouse to build machine learning models on Google Cloud Platform. Recommendation engines use user profiling and segmentation to suggest relevant content and ads. Natural language processing is used to build a Malay text processing library and automate tasks like document classification and brand safety scoring. The results include higher user engagement and time spent, and automating manual work.

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

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

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

4. Understand the business:

5. 01 Business goal
- Understand 10+ millions user behavior
& better serve them

6. User behavior shift

7. Customer journey & decision

8. Personalized user experience

9. 02 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 data

12. Machine learning on GCP

13. 03 Data Science
- Understand user behavior, serve them relevant content
- Recommendation engine

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

15. 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. Content Recommendation engine
User behavior & viewership User profile and segment Recommendation engine
trending
Content-based
User-based
collaborative
filtering (UBCF)

17. DEMO：
Recommendation engine

18. GCP CloudML

19. 04 Natural language processing
- Building local Malay text processing library
- Document classification, brand-safe score & other

21. 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 ….”

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

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

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

26. Automate Document classification & Brand-Safe score

27. Automate Document classification & Brand-Safe score (2)
Not safe for advertiser!! 

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

29. Build local knowledge graph

30. Local word embedding & word similarity

31. Local word embedding & word similarity (2)

32. DEMO：
Document classification &
brand-safe score

33. 05 Impact & Benefit
- Higher user engagement, better campaign performance
- Automate manual work such as document classification & label

34. +20% users +30% Page-views +25% user avg time spent
User profiling & content recommendation

35. 50000+ documents/contents auto-tagged
50+ man-hours saved per week
Improved content categorization
Improved campaign performance
NLP & automate content classification

36. Question?
#nextxkl #next18extended

37. Thanks! 
#nextxkl #next18extended

39. ML Workflow
#nextxkl #next18extended

40. NSTP article recommendation