CALL RECOMMENDATION SYSTEM AND CALL RECOMMENDATION METHOD BASED ON ARTIFICIAL INTELLIGENCE

A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.