Beyond who else bought what

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    10 Favorites & 1 Event

    Beyond who else bought what - Presentation Transcript

    1. Beyond Who Else Bought What
        • Andraz Tori, CTO
        • [email_address]
        • Twitter: andraz
    2. Overview
      • Why recommend anything?
      • Reader / Author recommendations
      • Simple tricks
      • Advanced solutions
      • Demos & Results
      • Q & A
    3. Why recommend?
        • Recommendations
        • =
        • Contextual advertising
        • ->
        • Monetization
    4. Why recommend?
        • Recommendations
        • =
        • Navigation & steering
        • ->
        • Monetization
    5. Why recommend?
        • Recommendations
        • =
        • Navigation & steering
        • ->
        • Pageviews
        • ->
        • Monetization
    6. Why care as a publisher? Readers
      • Direct monetization
      • Steering -> pageviews -> monetization
      • Better service (example: hiding spam)
    7. Why care as a publisher? Writers
      • Less non-creative workload
      • Gratification -> more and better conent (especially UCG authors)
      • Steering the authors (getting trackbacks, becoming part of the community)
      • We get better metadata (more chances for distribution / repurposing)
    8. What if recommendation goes wrong?
    9. It will go wrong!
    10. What if recommendation goes wrong?
      • Can it do damage?
      • How much is too much?
      • Whitelists & blacklists
      • Make author act as a filter/an editor
      • And at the same time offer him value
    11. How is it done?
      • Based on previous behaviour of this and other users
      • Based on understanding of the data being dealt with
      • Random
      • Combination of the three
    12. How do I do it?
      • Simple tricks
      • Ready-made web solutions
      • Third party APIs
      • Roll your own
    13. Simple tricks
      • Display random photos for UCG author to start with
      • Allow for quoting shortcuts (reblog)
      • Show writer's interest feed in post compositor
    14. Ready-made solutions
      • Tagaroo
        • WordPress server-side plug-in
      • Zemanta
        • Server or client side (wordpress.com, blogger.com, TypePad, WP, MT, Ning, LiveJournal, MySpace, DotClear, Drupal , Tumblr)
      • Kaalga
        • Firefox extension
    15. APIs you can use
      • Yahoo term extractor, BOSS, Flickr
      • Calais (tags, entities)
      • Zemanta (tags, images, in-text and related links)
      • Web services, free, commercial use allowed
    16. So you want to be a hero?
      • Entity extraction
      • Classification
      • Use open source libraries!
      • Content access APIs (Flickr)
      • Indexing, use Lucene and Solr
      My advice: just don't
    17. Demos
      • Tagaroo
      • Zemanta
      • Kaalga
      • Left out: Non-serendipity solutions
    18. Tagaroo 1
      • Discovers interesting keywords, offers links on click
    19. Tagaroo 2
      • Discovers interesting keywords, offers links on click
    20. Tagaroo 3
      • Discovers interesting keywords, offers links on click
    21. Tagaroo 4
      • Discovers interesting keywords, offers links on click
    22. Tagaroo 4
    23. Zemanta
      • Live Demo
    24. Demo
    25. Kaalga
      • Discovers interesting keywords, offers links on click
    26. Steering authors
      • Inter-links to your own properties
      • Links to relevant sites (becoming part of communities)
      • More images are used, less worries about „google images syndrom“
      • More linking to affiliates
      • More tags, categories
    27. Resulting in
      • Better positioning of your authors in the community
      • More affiliate monetization
      • Some bloggers 10-15% traffic increase
    28. Issues
      • Tower of Babel
      • Some users are overwhelmed
      • GUI is 80%
    29. The next web?
      • Understanding content more and more
      • Understanding the social context
      • First the advertising world was hit
      • Now this will trickle into publishing
    30. What if
      • Your computer understood what you are writing about
      • Know who you know, what you know
      • Know what you don't know
    31. How would your text editor look like? How would your text editor look like? How would your writing assistant look like?
    32. The next web?
      • ... will be like a great party host , introducing us to each other and bringing us together into meaningful conversation.
                      • Marta Strickland, Organic
    33. Q&A
        • Andraz Tori
        • [email_address]
        • Twitter: andraz

    + Andraz ToriAndraz Tori, 2 years ago

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