This document outlines several AI use cases at FD Mediagroep, a Dutch media company. It describes a personalized newsletter that recommends articles to readers using collaborative filtering. It also describes an entity linker that identifies organizations in news articles using named entity recognition and linking models. Finally, it discusses a "Smart Radio" project to personalize audio content delivery in their radio app.

1. Mining knowledge in news
for personalization
Dung Chu and David Graus,
data scientists
FD Mediagroep

2. Outline
• Introduction FDMG
• AI use cases:
– FD personalized newsletter
– CompanyInfo entity linker
– Smart Radio

3. FD mediagroep
Het Financieele Dagblad
Het Financieele Dagblad is dé nieuws- en inspiratiebron die op elk
moment van de dag financieel-economische betekenis geeft aan
ontwikkelingen in de wereld.
Company info geeft altijd real time toegang tot actuele bedrijfs- en
prospectinformatie van 2,5 miljoen bedrijven in Nederland
FD/BNR Networks
FD/BNR Networks brengt ideeën, meningen en talentrijke
ondernemers en professionals samen door de organisatie van o.a.
forums, debatten, netwerksessies, en evenementenreeksen.
BNR Nieuwsradio
BNR Nieuwsradio is de enige radiozender in Nederland waar
ambitieuze en ondernemende mensen 24 uur per dag op de hoogte
worden gehouden van relevant nieuws.
FD Mediagroep helpt om betere beslissingen te nemen door overzicht, inzicht én
(vooruit)zicht te bieden met journalistieke merken en hoogstaande kwalitatieve producten.

4. FD personalized newsletter

5. FD personalized newsletter: goals
• Diversify content sent to readers
• Improve engagement

6. FD personalized newsletters
• Rich data:
– Logged-in data with unique client ID
• Recommendation model: Item-to-item collaborative
filtering
Published
last week
Older
articles
PySpark:
+ Convert reader-article matrix to IndexedRowMatrix
+ columnSimilarities(): fast compute of cosine
similarity between columns

7. Results: free clients
T-test

8. Results: paid clients (fixed 2000)
T-test

9. Content diversification
Popularity emails
Personalized emails
7 articles 178 articles (60 clicked)
Long tail
articles
25000
15000

10. CompanyInfo entity linker
Ref: https://www.slideshare.net/davidgraus/financial-news-mining-pydata-amsterdam

11. 11

12. 12

13. Situation before
• Manual process
• Not able to process many articles

14. Rich data
1. Knowledge Base (Company.info):
– >2.8M organization profiles
– Rich metadata: sector information, financial information,
people, buildings, etc…
2. News articles:
– >1200 sources (Dutch, online)
• From Het Financieele Dagblad to the Groninger Gezinsbode
– Around ~4k articles per day
– Multiple years of hand-labeled archive

15. Model
• Two stage approach:
– Named-entity recognition
– Entity Linking using binary classification
• Predict whether mention m should be linked to entity candidate c

16. Pipeline
16
1. 0. 1. 1. 0.
Doc
NER mention1 org1
org2
org3
…
org n
Extract
Features
1. 0. 0. 1. 0.
1. 1. 0. 0. 0.
1. 0. 1. 1. 0.
RF
1
0
mention2
mention3
0.647
…
0.873
0.780
0.342
…

17. Pipeline
17
1. 0. 1. 1. 0.
NER mention1 org1
org2
org3
org n
Extract
Features
1. 0. 0. 1. 0.
1. 1. 0. 0. 0.
1. 0. 1. 1. 0.
RF
1
0
mention2
mention3
0.647
0.873
0.780
0.342
…… …

18. Pipeline
18
1. 0. 1. 1. 0.
NER mention1 org2
org3
org1
org n
Extract
Features
1. 0. 0. 1. 0.
1. 1. 0. 0. 0.
1. 0. 1. 1. 0.
RF
1
0
mention2
mention3
0.873
0.780
0.647
0.342
…… …

19. org3 1. 0. 1. 1. 0. 0.780
Pipeline
19
1. 0. 1. 1. 0.
NER mention1
org1
…
org n
Extract
Features
1. 0. 0. 1. 0.
RF
1
0
mention2
mention3 0.647
0.342
…
org2 1. 1. 0. 0. 0. 0.873
…

20. Evaluation
• Take data, make train/test-split
– NER: ~85%
– EL: ~85%
• But: Data is noisy/biased
– + Manual inspection
20

21. Results
– Replace previous manual process
– Number of articles per day: approx. +160%
– Number of linked orgs: approx. +310%
– More “long tail” articles
21

22. Smart Radio

23. Smart Radio – audio personalization
• Current audio listening experience:
– Passive listening
– Long audio data with mix of topics
• New way:
– On demand and personalized listening
TV
Radio

24. Smart Radio - BNR app

25. Smart Radio – pipeline “serverless”

26. Take-home messages
• AI projects add values to existing businesses
• Diversify content delivered to customers: enable
“long tail” articles/audio fragments
• In combination with rich data, simple AI models work
• Our solutions are re-usable

27. Upcoming projects
• Fully automatic audio segmentation
• Personalized summarization of news articles

28. Twitter: @dungchu
Email: dung.manh.chu@fdmediagroep.nl
LinkedIn: https://www.linkedin.com/in/dungmanhchu/