Use Sherpa.ai to predict the next position of a user, based on their daily routine. With Sherpa.ai’s context-aware location prediction technology, you can deduce points of interest (Home, Office or different Leisure points of interest) using historical and habitual information, allowing you to make predictions based on user location.
In order to be able to create recommendations, make predictions, and surpass the state-of-the-art, Sherpa.ai has developed hybrid models that combine Machine Learning, Bayesian, and Computational techniques, such as Neural Networks, Random Forest, and Latent Variable Models.
With the Sherpa.ai Next Place Prediction API, you can predict the next position of a user based on their daily routine. For example, you can say that a user will be at the office on Monday at 12:00 PM with 98% probability.
First, track a user’s position over a period of time, in order to deduce their points of interest: Home, Work, and Leisure. If you have a prediction of the user’s location at a certain time and you know their POIs, you can make proper recommendations to them, like “Take Highway 19.”
When the system has enough data, it will predict the next possible route based on the user’s daily routine and historical habits. For example, if the user has an appointment in their calendar with a location, the next route will take them to it.