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In this presentation, I and 3 fellow Carnegie Mellon graduate students present our findings on Foursquare mayorships, and their diversity in location and venue type.Using this data, we develop an efficient market model for mobile advertising on Foursquare and perhaps other platforms.
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Diversity of Foursquare Mayorships:
Venue Types and Venue Locations
Troy Effner
Haris Krijestorac
Alan Pan
Vishal Patel
What is Foursquare?
• Location-based social networking platform for mobile devices
• Users ‘check in’ to venues
• User with most checkins to a venue becomes its ‘mayor’
Motivation for Research
• Location-based advertising– How much value equity is there in targeting
local venues or venues of similar type?
Objective
• Based on Foursquare user data, create an efficient model for mobile advertising
Gathering Data
• Location– API returns city, state, zip, and longitude
and latitude coordinates– Limited to mayors of Pittsburgh locations
• Venue Type– API returns ‘categories’ and
‘subcategories’
Data Overview
• 557 users with > 1 mayorship
• 1897 mayorships Total
• Average of 3.4 mayorships per person
• 2536 Venues
Measuring Location Diversity
Location Diversity Metrics
• Given a set of locations for which a user is mayor, find the centroid
Centroid:Geometric center of shapeformed by all points of mayorship
Location Diversity Metrics
• Average distance from centroid
• Largest and smallest distance from centroid
• Variance in distances from centroid
Location Diversity Data
Metric Value (in miles)
Mean Distance from centroid 0.58
St. Dev. Distance from centroid 0.57
Range of distances from centroid
[0.002, 1.875]
Users hold mayorships over pretty diverse locations!
Measuring Venue Type Diversity
Venue Type Diversity Metrics
Venue Category
Arts Food Nightlife
Venue Subcategory
Museum Ice Cream Lounge
Note that there are venue categories and venue subcategories
…We just use categories
Measuring Venue Type Diversity
# Unique venue categories where user is mayor
÷Total # venues where user is mayor
Metric value is proportional to diversity
Venue Type Diversity Example
• Low Diversity User– Mayor of 10 venues of 2 unique types (Arts, Food)
2 ÷ 10 = 0.2
• High Diversity User– Mayor of 10 venues, 8 unique types
8 ÷ 10 = 0.8
Venue Type Diversity Data
Metric Value
Avg. Venue Diversity
0.72
St. Dev. Venue Diversity
0.24
Users hold mayorships in diverse types of venues!But users are pretty similar in their level of diversity.
Interesting Findings
Popularity of Venue Types
• Most popular– Home (4.7%)– Office (4.3%)– Bar (2.1%)
• Least popular– Dorm (.007%)– Bus Station(.008%)– Bank (.009%)
Power User: Matt A.• 35 Pittsburgh Mayorships
– 2 Dive bars, 6 homes, 2 tattoo parlors, mayor of Mt. Washington
• Matt drives to his friends homes before he goes with them to dive bars and diners. He doesn’t stay at these dive bars for long and hops from one to another.
Power User: Derek• 20 Pittsburgh Mayorships• Mayor of two hotels and one bar within a hotel. Rides
the bus to work and buys coffee. Enjoys going to the parks and landmarks as well as the Strip district.
Acknowledging our Challenges
Do Mayors have Different Habits in Different Cities?
Solution:
Do same research across various cities.
If patterns are different, localize.
If not, generalize.
Null Values in our Data
• About 25% venue types are null
• Is there anything special about the nulls?– Privacy conscious people?– Venues that did not fall into a category?– Self-created venues?
Multiple Centroids?
Applying our research
• How relatively likely is a specific user to pursue mayorship of a specific venue – In a specific location– Of a specific venue type
Targeting Function
• Function that calculates how targeted an ad for a specific venue would be to a specific user?
Targeting Function Inputs• User Inputs:
– Mayorship Centroid (UM)– Location Diversity (ULD)– Venue Type Diversity (VVD)
• Venue Inputs– Venue Type (VT)– Venue Location (VL)
• User - Venue Inputs– % of user’s mayorships of the same type as that of the
venue (UVV)– Distance between UM & VL (UVL)
Targeting Function
Value of an ad to User U from Venue V
= UVV (1-UVD) + UVL (1.875-ULD)
Proportional to % mayorships of same typeAs venue
Inversely proportional touser’s venue diversity
Proportional to distance betweenvenue location and mayorshipcentroid
Inversely proportional touser’s location diversity
Advertiser’s Perspective
Advertisers can take different approaches…
• Broad Targeting – Serve ads to many low-relevance foursquare users– Good for promoting awareness
• Specific Targeting– Serve ads to few highly relevant foursquare users– Good for bringing in customers
Advertisers can specify a level of relevancy
Foursquare’s Perspective
Foursquare can charge advertisers according to their specified level of relevancy
Advertisers will reach out to the right audience
Foursquare can charge in accordance with advertiser’s goals
Creates efficient market for location-based advertising
Q&A