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Web Personalization:
Powerful Information Tool
or Filter Bubble?



 Donna Witek
 Public Services Librarian
 Weinberg Memorial Library
 April 18, 2013
Personalization...




  Where everybody knows your name.
Personalization and Customization
•   Vendor-initiated vs. user-initiated
•   adaptivity vs. adaptability
•   passive vs. active

Types of algorithm
    o   content-based
             •   user-profiles
    o   collaborative/hybrid
             •   rules-based
             •   recommendations
    o   social-based
    o   demographic/context-based
    o   knowledge-based
Google




         (Ads were at the bottom of the page.)
Facebook




           (No two News Feeds are alike!)
Amazon, Netflix, and Pandora




(Shopping/entertainment = recommendations/ratings)
Image by Toothpaste for Dinner (10/06/11)
"The new generation of Internet filters looks at the things you seem to
like--the actual things you've done, or the things people like you like--
and tries to extrapolate. They are prediction engines, constantly
creating and refining a theory of who you are and what you'll do and
want next. Together, these engines create a unique universe of
information for each of us--what I've come to call a filter bubble--
which fundamentally alters the way we encounter ideas and
information." --Eli Pariser, The Filter Bubble, p. 9




    dontbubble.us
                                                               Image from The Telegraph
   duckduckgo.com
Thank you

Questions or comments?

Donna Witek
donna.witek@scranton.edu

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Web Personalization: Powerful Information Tool or Filter Bubble?

  • 1. Web Personalization: Powerful Information Tool or Filter Bubble? Donna Witek Public Services Librarian Weinberg Memorial Library April 18, 2013
  • 2. Personalization... Where everybody knows your name.
  • 3. Personalization and Customization • Vendor-initiated vs. user-initiated • adaptivity vs. adaptability • passive vs. active Types of algorithm o content-based • user-profiles o collaborative/hybrid • rules-based • recommendations o social-based o demographic/context-based o knowledge-based
  • 4. Google (Ads were at the bottom of the page.)
  • 5. Facebook (No two News Feeds are alike!)
  • 6. Amazon, Netflix, and Pandora (Shopping/entertainment = recommendations/ratings)
  • 7. Image by Toothpaste for Dinner (10/06/11)
  • 8. "The new generation of Internet filters looks at the things you seem to like--the actual things you've done, or the things people like you like-- and tries to extrapolate. They are prediction engines, constantly creating and refining a theory of who you are and what you'll do and want next. Together, these engines create a unique universe of information for each of us--what I've come to call a filter bubble-- which fundamentally alters the way we encounter ideas and information." --Eli Pariser, The Filter Bubble, p. 9 dontbubble.us Image from The Telegraph duckduckgo.com
  • 9.
  • 10. Thank you Questions or comments? Donna Witek donna.witek@scranton.edu