Personalized merchant scoring based on vectorization of merchant and customer data

Provided are various mechanisms and processes for generating dynamic merchant scoring predictions. A system is configured to receive datasets comprising pairings between training customer profiles and training merchant profiles. For each pairing, a set of feature values corresponding to features specified by the customer and merchant profiles are extracted and converted into a training vector to train a machine learning model to determine a weighted coefficient for each feature. Once sufficiently trained, the system determines a set of available merchant profiles for a customer profile in response to receiving a search request from a customer associated with the customer profile. For each pairing between the customer profile and an available merchant profile, the system determines an order score for the available merchant based on the weighted coefficients and an input set of feature values specified by the customer profile and the available merchant profile.