2. My Background
● Software Engineer/Data Scientist
● Machine learning team
● At Meetup since May 2012
● BS Computer Science
○ Information Retrieval
○ Data Mining
○ Math
■ Linear Algebra
■ Graph Theory
4. What this talk is
● Super secret peek into Meetup!
● Meetup recommendations examples
● How we do recommendations
(model/features)
● Lessons learned/what’s next
5. What this talk isn’t
● What is a data scientist?
● What is big data?
● How does matrix factorization or gradient
boosted decision trees or map reduce or this
framework I hope you’ll use work?
6. Why Meetup data is cool
● Real people meeting up
● Every meetup could change someone's life
● No ads, just do the best thing
● Oh and 114 million rsvps by >14 million
members
● 2.7 million rsvps in the last 30 days
○ ~1/second
7.
8. Data at Meetup
● User data
● Site monitoring/performance
● AB testing
● Recommendations*
9. “Everything is a recommendation”
● Not my phrase
● Not actually true yet
● Working on it
13. Topic Recommendations
● New registrant
● Don’t know anything about you yet!
● Most popular is boring/repetitive
Algorithm:
○ Group local meetups by topic
○ Select topic with most groups
○ Remove those groups
○ Repeat
18. Why Recs at Meetup are hard
● Incomplete Data (topics)
● Cold start
● Asking user for data is hard
● Going to meetups is scary
● Sparsity
○ Location
○ Groups/person
○ Membership: 0.001%
○ Compare to Netflix: 1%