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User behavior based
recommendation
THE PARSERS ( Kunal, Karan & Anupam )
How we do presently
• Presently we use solr content based recommendation.
• Solr does content matching and give recommenda...
Drawback
• We don’t know how relevant are the documents
that are being suggested to the user.
New Approach
• Track Users browsing behavior.
• Whenever a user visits a slideshow generate an
event which contains the
sl...
Benefit
• The new set of recommendations might have a
loose coupling in terms of content but is a set of
slideshows which ...
Demo
Steps we took
• Setup one node Hadoop Cluster
• Imported the user behavior log of 1 week in the format
• (user_id, slidesh...
Q&A
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The parsers & test upload

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The parsers & test upload

  1. 1. User behavior based recommendation THE PARSERS ( Kunal, Karan & Anupam )
  2. 2. How we do presently • Presently we use solr content based recommendation. • Solr does content matching and give recommendation based on content.
  3. 3. Drawback • We don’t know how relevant are the documents that are being suggested to the user.
  4. 4. New Approach • Track Users browsing behavior. • Whenever a user visits a slideshow generate an event which contains the slideshow_id, user_id, platform • These data would be used to analyze the related content. • Apache mahout uses collaborative filtering and gives us the recommendation.
  5. 5. Benefit • The new set of recommendations might have a loose coupling in terms of content but is a set of slideshows which have a higher priority for the user. • Would help us in attaining better engagement.
  6. 6. Demo
  7. 7. Steps we took • Setup one node Hadoop Cluster • Imported the user behavior log of 1 week in the format • (user_id, slideshow_id) • Setup Apache Mahout and compiled it using maven • Apache mahout analyzed it for 2-3 hrs and generated an output in format • (slideshow_id, recommended_slideshow_id, relevence_score) • Used this output to show the recommended content.
  8. 8. Q&A

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