Data Mining as an Engine of Personalization


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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.

Published in: Technology, Education
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  • The web is going towards personalization – no one wants a flat experience anymore
  • How we’ll capture the data: Start with base linguistics Extend with available extras
  • Language gets wonky without stop words
  • Use HTML5 LocalStorage & Cookie backup
  • Data Mining as an Engine of Personalization

    1. 1. ENTERPRISE IT 20 x 20 Data Mining as an Engine of Personalization Jonathan LeBlanc (@jcleblanc)
    2. 2. The Web is Becoming Personal
    3. 3. Premise You can determine the personality profile of a person based on their browsing habits
    4. 4. Then I Read This… Us & Them The Science of Identity By David Berreby
    5. 5. 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
    6. 6. Technology was NOT the Solution Identity and discovery are NOT a technology solution
    7. 7. Our Subject Material
    8. 8. 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
    9. 9. The Basic Pieces Page Data Scrapey Scrapey Keywords Without all the fluff Weighting Word diets FTW
    10. 10. Capture Raw Page Data Semantic data on the web is sucktastic Assume 5 year olds built the sites Language is the key
    11. 11. Extract Keywords We now have a big jumble of words. Let’s extract Why is “and” a top word? Stop words = sad panda
    12. 12. Weight Keywords All content is not created equal Pay special attention to high value tags & content location
    13. 13. Expanding to Phrases 2-3 adjacent words, making up a direct relevant callout Seems easy right? Just like single words
    14. 14. Working with Unknown Users The majority of users won’t be immediately targetable
    15. 15. Tracking Emotional Change You have to be aware of personality changes Tracking users as they use your service
    16. 16. Using On Demand Tracking Traits of the Bored Distraction Repetition Tiredness Reasons for Boredom Lack of interest Readiness
    17. 17. Adding in Time Interactions Time and interaction need to be accounted for Gift buying seasons see interest variations
    18. 18. Grouping Using Commonality Interests User A Interests User B Interests Common
    19. 19. A Closing Thought Just because you can do something, doesn’t mean you should