We made this for frequent travelers, productivity geeks, who prefer to make quick and data-informed decisions on where to go next.
It is built on MERN stack, AWS ML services (for matching and recommendations), AWS ElastiCache (for Ranks). We use Foursquare API to import the user checkins. The ML model was trained on an anonymized checkins dataset obtained from Foursquare. It contains 33M of checkins, created by 227K users in 415 most popular cities worldwide.
We made this for frequent travelers, productivity geeks, who prefer to make quick and data-informed decisions on where to go next.
It is built on MERN stack, AWS ML services (for matching and recommendations), AWS ElastiCache (for Ranks). We use Foursquare API to import the user checkins. The ML model was trained on an anonymized checkins dataset obtained from Foursquare. It contains 33M of checkins, created by 227K users in 415 most popular cities worldwide.