People are no longer satisfied with flat, single-output websites that do not personalize to the needs and differences of each viewer. With the wealth of data and interaction mining techniques being employed in everything from online sites to brick and mortar stores, we are truly seeing a major industry shift towards automatic personalization. This session will cover the concepts of long-term personalization and on-demand emotional state interaction, which in turn can be used as the architecture to drive commerce and personalization.

1. ENTERPRISE IT 20 x 20
Data Mining as an Engine of
Personalization
Jonathan LeBlanc (@jcleblanc)

2. The Web is Becoming Personal

3. Premise
You can determine the personality
profile of a person based on their
browsing habits

5. Then I Read This…
Us & Them
The Science of Identity
By David Berreby

6. Different States of Knowledge
What a person knows
What a person knows they don’t know
What a person doesn’t know they don’t
know

7. Technology was NOT the Solution
Identity and discovery are
NOT a technology solution

8. Our Subject Material

9. HTML content is poorly structured
There are some pretty bad web
practices on the interwebz
You can’t trust that anything
semantically valid will be present

11. The Basic Pieces
Page Data
Scrapey
Scrapey
Keywords
Without all
the fluff
Weighting
Word diets
FTW

12. Capture Raw Page Data
Semantic data on the web
is sucktastic
Assume 5 year olds built
the sites
Language is the key

13. Extract Keywords
We now have a big jumble
of words. Let’s extract
Why is “and” a top word?
Stop words = sad panda

14. Weight Keywords
All content is not created
equal
Pay special attention to
high value tags & content
location

15. Expanding to Phrases
2-3 adjacent words, making
up a direct relevant callout
Seems easy right? Just like
single words

16. Working with Unknown Users
The majority of users won’t
be immediately targetable

17. Tracking Emotional Change
You have to be aware of
personality changes
Tracking users as they
use your service

18. Using On Demand Tracking
Traits of the Bored
Distraction
Repetition
Tiredness
Reasons for Boredom
Lack of interest
Readiness

19. Adding in Time Interactions
Time and interaction need
to be accounted for
Gift buying seasons see
interest variations

20. Grouping Using Commonality
Interests
User A
Interests
User B
Interests
Common

21. A Closing Thought
Just because you can do something,
doesn’t mean you should