This document provides an overview of machine learning and data recommendations at Meetup.com. It discusses how Meetup uses machine learning models like logistic regression and collaborative filtering to provide topic and group recommendations to users based on their location, interests, and other attributes. Features are engineered using data like topic matches, Facebook friends, and distances between locations. Challenges include cold starts, sparsity of data, and cleaning issues. Future work may include recommendations based on clicks and impressions as well as people-to-people recommendations.

1. Machine Learning and
Data at Meetup
Evan Estola
Meetup.com
evan@meetup.com
@estola

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

3. You
● Data Scientists?
● Engineers?
● Statisticians?
● Students?
● Non-technical?

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

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

10. Recommendation

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

16. Group/Event Recommendations
● Replaced a topic only system
● Inputs:
○ Member, location, topics, facebook friends?
demographics?
● Outputs:
○ Ranking

17. Collaborative Filtering
● Classic recommendations approach
● Users who like this also like this

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%

19. Supervised Learning/Classification
● “Inferring a function from labeled training
data”
● Joined Meetup/Didn’t join Meetup
● “Features”

20. Topic Match

21. State Match

22. Logistic Regression
● Score
○ “Probability”
○ Ranking
● Fast + Easy
● Weights!

23. Group recommendation weights
● TopicMatch 1.21
● TopicMatchExtended 0.17
● FacebookFriends 0.15
● SecondDegreeFacebook 0.79
● AgeUnmatch -2.20
● GenderUnmatch -2.6
● StateMatchFeature 0.44
● CityMatch 0.02
● DistanceBucket <2 1.39
● DistanceBucket 2-5 0.83
● DistanceBucket 5-10 0.60
● DistanceBucket >10 n/a

24. Making up features
● “Zipscore”
● All topics not created equal
● Facebook likes

25. Real data is gross
● Preprocessing is critical!
○ missing data
○ outliers
○ log scale
○ bucketing
○ selection/sampling (not introducing bias)

26. Cleaning data
● Schenectady
● Beverly Hills
● Astronaut
● Fake RSVP boosts (+100 guests!)
● Rsvp hogs

29. TO THE FUTURE!
● Hadoop
● Clicks
● Impressions
● People to people recommendations?
● Recommending people to groups?

30. Thanks!
Smart people come work with me.
http://www.meetup.com/jobs/
Special thanks:
● Chris Halpert
● Victor J Wang