data features at foursquare (august 2013)
TRANSCRIPT
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From the data point of view...
Check-in = User + Location + Time
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From the data point of view...
Check-in = User + Location + Time4.5+ Billion check-ins, 6 Million check-ins/day
2013
From the data point of view...
Check-in = User + Location + Time4.5+ Billion check-ins, 6 Million check-ins/day
40 million users worldwide60 million locations (venues)
2013
Data features●Explore●Ads relevance●Check-in Search●Proactive recommendations●Where to go next?●Similar Venues●Tourist Venues●Venue Shapes●Closed Venue detection●etc.
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Explore
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Explore
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Explore
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What powers Explore?
Signals:●Venue-specific●Temporal ●User-level (personalized) ●Social ●Location-specific●Query text●…...
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Venue Rating
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Venue Rating
• Like %
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Venue Rating
• Like %• Popularity
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Venue Rating
• Like %• Popularity• Loyalty
2.6 check-ins/user Vs. 1.71 check-ins/user
Vs.
Vs.
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Venue Rating
• Like %• Popularity• Loyalty• Expertise Score
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Venue Rating
• Like %• Popularity• Loyalty• Expertise Score• Tip Sentiment• Listiness• …
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Temporal Signals
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●Based on the user’s own history●Where have you been?●What types of places do you go to?●Are you familiar with this area? Are
you a tourist?
User Signals
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Social Signals
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Social Signals
The chance that an average user checks in at a place their friends have never been < 40%
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Other features
There are a lot of other signals that go in Explore
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The Venue Graph
•30m venues interconnected w/ different signals:
•flow•co-visitation•categories•menus•tips and shouts
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Tourist Venues
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Venue Shapes
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Closed Venue Detection
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The Data tell us...
• Human mobility patterns– Spatial– Temporal
• User context– Where they are, where they’ve been,
where they’re (likely to be) going– What they like, what their friends like
• Venue patterns– Who goes here, when, and what they
like
