A mobile social network can recommend events of interest by analyzing information from users with similar profiles. For example:
Miguel, a gay 30 year old New Yorker vacationing in San Francisco, wakes up on Sunday morning wondering what to do. His cell phone beeps with a text message suggesting that he visits Dolores Park later that afternoon.
This suggestion was made because his social network has data that on hot and sunny Sunday afternoons in San Francisco, hundreds of gay men in a social network spend time in Dolores Park.
Let’s deconstruct how the social network arrived at this conclusion:
- In the past 6 months, hundreds of people used their mobile device to access the network from Dolores Park. It knows this because the phone communicates the user’s GPS coordinate. Many phones already have this capability, either built in or through a bluetooth connection with a GPS.
- Whenever a member accesses the social network, it logs the time. Many of these accesses from Dolores Park occur on Sunday afternoons, between 2pm and 5pm.
- On each access, the network contacts an online weather service and logs the weather condition and temperature. Many of these accesses from Dolores Park on Sunday afternoons occur on sunny days above 65°.
- Many of these network accesses correspond to gay men between 25 and 40 years old, as specified in their user profile under “orientation” and “age”.
By collecting data about how and when members use a service, social networks can creatively analyze and find patterns useful to the community. Furthermore, when Miguel arrives at Dolores Park, I would expect the network to facilitate a meeting with other like-minded members nearby.
Update: Related to this, check out this posting describing GyPSii, a social network that tracks users’ GPS location