Abstract: Provided are various mechanisms and processes for generating dynamic merchant similarity predictions. In one aspect, a system is configured for receiving historical datasets that include a series of merchants from historical browsing sessions generated by one or more users. The merchants are converted into corresponding vector representations for training a predictive model to output associated merchants based on a generated weighted vector space. Once sufficiently trained, data from a new browsing session may be received, which may include a target merchant. The target merchant is input into the predictive model as a vector to output one or more context merchants having vectors with the highest cosine similarity value to the target merchant vector. Selected context merchants may then be transmitted to the user device as targeted merchant suggestions in the new browsing session. The predictive models may be continuously trained using data received from subsequent browsing sessions.

Abstract: Provided are various mechanisms and processes for generating dynamic merchant similarity predictions. In one aspect, a system is configured for receiving historical datasets that include a series of merchants from historical browsing sessions generated by one or more users. The merchants are converted into corresponding vector representations for training a predictive model to output associated merchants based on a generated weighted vector space. Once sufficiently trained, data from a new browsing session may be received, which may include a target merchant. The target merchant is input into the predictive model as a vector to output one or more context merchants having vectors with the highest cosine similarity value to the target merchant vector. Selected context merchants may then be transmitted to the user device as targeted merchant suggestions in the new browsing session. The predictive models may be continuously trained using data received from subsequent browsing sessions.

Abstract: 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.

Abstract: 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.

Abstract: Provided are various mechanisms and processes for generating dynamic merchant similarity predictions. In one aspect, a system is configured for receiving historical datasets that include a series of merchants from historical browsing sessions generated by one or more users. The merchants are converted into corresponding vector representations for training a predictive model to output associated merchants based on a generated weighted vector space. Once sufficiently trained, data from a new browsing session may be received, which may include a target merchant. The target merchant is input into the predictive model as a vector to output one or more context merchants having vectors with the highest cosine similarity value to the target merchant vector. Selected context merchants may then be transmitted to the user device as targeted merchant suggestions in the new browsing session. The predictive models may be continuously trained using data received from subsequent browsing sessions.