1 / 21

Data Mining as an Engine of Personalization

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.

jcleblanc
Download Presentation

Data Mining as an Engine of Personalization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

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

  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. Technology was NOT the Solution Identity and discovery are N O T a technology solution

  7. Our Subject Material

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

  9. The Basic Pieces K e y w o r d s Without all the fluff We i g h t i n g Word diets FTW P a g e D a t a Scrapey Scrapey

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

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

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

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

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

  16. Using On Demand Tracking T r a i t s o f t h e B o r e d Distraction Repetition Tiredness R e a s o n s f o r B o r e d o m Lack of interest Readiness

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

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

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

More Related