Kevin Cohn presented on how Atypon, a provider of content delivery software for publishers, can improve research efficiency by using their data and automatic topic modeling. Atypon delivers over 15 million journal articles and their Literatum platform sees 1.5 billion user sessions annually. By tapping into this data to better understand topics and individual users, Atypon can provide a simple search interface that calculates relevancy for each user to efficiently connect them with relevant content. This improves research efficiency by reducing time spent searching and increasing time for reading.

1. Kevin Cohn
Chief Operating Officer
@Atypon
Improving Research
Efficiency
Academic Publishing in Europe, Berlin
30 January 2013
User and Content Fingerprinting

3. • Provider of Software as a Service content
delivery for publishers
• Literatum platform used to deliver 15M journal
articles and 70,000 eBooks
• 1.5 billion user sessions in 2012
About Atypon
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4. • Research efficiency can be greatly improved if
publishers tap into their huge volume of data to
better connect users to content.
Thesis
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6. Users don’t want “advanced search...”

8. ...but they do want relevant results.

9. This is the APE I’m looking for.

10. Data can drive this behavior.

11. • Relevancy is the only order that matters
• > 50% of clicks are to the first result
• > 90% of clicks are on the first page
• Filters/facets aren’t used
Observations
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12. • Give users what they want: a simple, Google-
like search interface
• But use proprietary data to calculate relevancy
for each individual user
Objectives
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13. Automatic Topic Modeling
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14. • Based on a statistical model called latent
Dirichlet allocation (LDA)
• Creates “topics:” collections of words that occur
together with great frequency
Topic #1: {mammal, primate, hominoidea}
Topic #2: {academic, publishing, europe}
Automatic Topic Modeling
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17. Topic #1

18. Topic #2

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25. • My search for “APE” returns results about this
conference, not primates
• The same is true for recommendations
• Better related articles (topics 1 and 2 are not
related, despite sharing “APE”)
Applications
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26. • Topics are self-updating = low-cost, low-
maintenance
• Flat (not hierarchical) = avoids troublesome
questions about classification
• Probabilistic (not binary) = better at expressing
relevancy to topics
Not a Taxonomy/Ontology...
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29. • Topics are “collections of words that occur
together with great frequency”
• Knowing that “APE” is an acronym for
“Academic Publishing in Europe”
• Knowing that “CC0” and “CC BY” are Creative
Commons license types
...But Is Helped by Them
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30. • We didn’t invent ATM (or LDA)
• Our implementation started as a collaboration
with academic researchers...
• ...and will require considerable experimentation
and testing to get right
Worth Mentioning
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31. • Usage is not personally identifiable
• Usage is not shared with third parties
• Users can opt out of personalization
Privacy
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32. • ATM uses proprietary data to calculate
relevancy for each individual user
• Gives users what they want: a simple, Google-
like search interface
• Improves research efficiency by freeing up
searching time for reading
Summary
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33. Thank You
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KCohn@Atypon.com
Kevin Cohn
Chief Operating Officer, Atypon