This document discusses personalization and recommendation engines. It explains the difference between explicit and implicit personalization, as well as first party and third party data. It also discusses challenges like data brokers collecting extensive personal data without user consent, and filter bubbles that isolate users. The document advocates for transparency, accountability, and giving users more control and influence over personalized experiences.

1. Escaping the
Echo ChamberThe User Experience of Data-driven Personalisation

3. Public Broadcasting
vs
Personalized
Recommendation
Engines
…when worlds collide.

4. * Terminology & Technology
* Experiences & Politics
* Decisions & Power
* UX Thinking

5. Terminology
& Technology
What is what

6. Explicit Personalisation
vs.
Implicit Personalisation
1st party data
vs.
3rd party data
Personalisation does
not require a login!
(But it can help)

7. Is there anything
you can
recommend?
Recommendations based on data:
Time of Day, Day of Week, Taste, Mood,
Rental History, Feedback on past rentals,
Favourite Genres, Context of Watching,
New Releases…

10. https://qz.com/571007/the-magic-that-makes-spotifys-discover-weekly-playlists-so-damn-good

12. Size of item pool
#new published
and unpublished
items per day
Identifying potential for automized personalization

13. User-based
collaborative filtering
Item-based
collaborative filtering
Utilizing collaborative filtering to drive
recommendations

14. Machine
Learning Engine
Data
Measurable Goal
e.g. conversion to a certain event
* Take data
* Build model
* Test against goal
* Rinse and repeat
e.g. data on past usage

15. Experiences
& Politics

17. Thanks - not interested
Please buy it!Please buy it!Please buy it!
Buy the sofa already!
Buy the sofa already!
Buy the sofa already!
Buy the sofa already!
Buy the sofa already!
Surprise – Sofa!

18. Mental Models

19. Two Problems
* #1 Brokers
* #2 Bubbles
Actually there is also ethics in general – but that’s the next chapter…

20. Source: https://www.ftc.gov/system/files/documents/reports/data-brokers-call-transparency-accountability-report-federal-trade-commission-may-2014/140527databrokerreport.pdf
Problem #1 Brokers

21. Source: https://www.ftc.gov/system/files/documents/reports/data-brokers-call-transparency-accountability-report-federal-trade-commission-may-2014/140527databrokerreport.pdf
Problem #1 Brokers
Age
Religious Affiliation
Technology Interest
Expectant or New Parent
Gender
Political Affiliation
Social Media Usage
Real Property Attributes
Height
Household Income
Vehicle Ownership
New Mover/Renter/Owner
Weight
Net Worth
Credit Card Usage
Discount Shopper
Race
Marital Status
Vacation Habits
High-End Shopper
Ethnicity
Biker
Cholesterol Focus
Home Loan Type
Occupation
Presence of Children
Diabetes Interest
Investment Habits
Soon-to-be High School Graduates
School-aged Children
Smoker in Household
Gambling
Guns and Ammunition Purchases
Home Ownership Status
Buy Disability Insurance
Lenses or Contacts
Brand Medication Conscious
Stuff brokers know

22. Source: https://www.ftc.gov/system/files/documents/reports/data-brokers-call-transparency-accountability-report-federal-trade-commission-may-2014/140527databrokerreport.pdf
Problem #1 Brokers

23. me
Problem #2 Bubbles

24. Problem #2 Bubbles

25. "Pretty much every
message that Trump put
out was data-driven."
Alexander Nix - CEO of
Cambrige Analytica

26. Serendipity

27. Decisions &
Power

28. „A squirrel dying in front of your house
may be more relevant to your interests
right now than people dying in Africa.“
Mark Zuckerberg

29. Aspiration vs. Impulse
Quinoa or Burger
arte or RTL II
Sport or Sofa

30. Computer says no!

31. remember…
it’s a black box

32. Paper: https://osf.io/zn79k/
“Deep neural networks are more accurate than humans
at detecting sexual orientation from facial images”
91 %
Accuracy

33. Pistacchio Taste Revelation Box (PTRB)
90 %
Accuracy
50 % Correct
Results
10 % prevalence of hidden pistacchio taste

34. Kate Crawford
@katecrawford
Data Ethics

35. UX Thinking
A proposal

36. Discover Define Generate Refine
Agile
Development
Proect
Incubation
Vision
* Shared Understanding
* Bricolage
* Critical Thinking
* Design Responsibility
Four ideas shape UX Thinking:

37. Technology
Solution
It is our job to make sure
our designs are centered
around real human needs.
The three dimensions of digital experiences

38. „Welcome to UX,
I‘ll be your guide!“
Conformity to
Expectation
Orientation must be
maintained
Transparency
Where is the
service personalized?
Traceability
What is the basis?
Which data is used?
Controllabillity
Give influence and
control to the user.

39. & Thx.
@ScreaminHias
https://www.xing.com/profile/Hias_Wrba
https://www.linkedin.com/in/mathias-wrba-82208937/
hello@hiaswrba.net