Building a recommendation engine, foursquare style
Last summer, foursquare's employee count had grown a bit beyond our office capacity (as we surged towards 20 employees) and we had people sitting in whatever open space we could find. We were split between floors, parked on folding tables, and crammed into couches and loveseats.
Context & Ripple Effects
Foursquare is writing engineering posts from a company straining at its seams. After the May 2010 inside look at the startup's early rise, headcount pushed past 20 and staff spilled across floors onto folding tables, couches, and loveseats — with the November 2010 westward expansion adding a second coast to the sprawl.
Against that backdrop, the post confirms Foursquare is building its recommendation engine in-house. That is a bet that the check-in log it has been accumulating since launch is valuable enough to power suggestions about where users should go next — turning a social game mechanic into a data product.
First-order effects
- Foursquare's accumulated check-in history stops being just a feed of friend activity and becomes training material for automated place recommendations, raising the bar for how much data the product must keep collecting.
- With more than 20 employees already overflowing two floors of improvised seating, the engineering team's shift toward recommendation work competes directly for scarce headcount against feature and scaling work.
Second-order effects
- Recommendation quality depends on check-in volume, so incentives inside the product tilt toward mechanics that get users checking in more often — retention loops become data-acquisition infrastructure.
- An in-house build means Foursquare owns the tuning and the data pipeline outright, keeping its behavioral dataset off third-party platforms and preserving optionality over how the engine is exposed in the product.
Third-order effects
- If the pattern holds, location-based startups differentiate less on check-in gimmicks and more on who owns the deepest record of where people actually go — proprietary behavioral data becoming the durable asset in local discovery.
- Building recommendation systems on top of one's own activity logs points toward a broader industry structure in which consumer apps double as data-collection engines whose value compounds with usage.
The trend: Check-in services are evolving from social games into data-driven discovery products, with each operator's own activity log as the raw material for recommendations.