Abstract: Provided are systems and methods that rely on machine learning to recommend employment opportunities. In one example, a method may include identifying, via execution of a first machine learning model, income data and spending data of a user, identifying, via execution of a second machine learning model, skill attributes of the user, determining, via execution of a third machine learning model, a recommended job for the user, where the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model, and displaying, via a user interface, a description of the recommended job.

Abstract: Provided are systems and methods for identifying a counterpart entity of a transaction from a transaction string. In one example, a method may include identifying a transaction string from an account of a target entity, the transaction string comprising a record of a payment that changed a balance of the account, executing a machine learning model based on the transaction string to determine a counterpart entity of the transaction with respect to the target entity, wherein, during execution, the machine learning model receives the transaction string as input and outputs an identifier of the counterpart entity, generating a data structure comprising a first field that stores the transaction string and a second field that stores an identifier of the counterpart entity, and storing the data structure within a file.

Abstract: Provided are systems and methods for generating propensity-driven employment-based search results. In one example, a method may include retrieving consumption data of a user associated with the user device and geographic location data of at least one of the user and the user device, the consumption data comprising behavior of the user captured over time, predicting one or more value-generating opportunities for the user from among a pool of value-generating opportunities via execution of a predictive model, where the predictive model determines a propensity of the user to respond to the value-generating opportunities based on the behavioral attributes included in the retrieved consumption data of the user and the geographical location data, and transmitting the one or more predicted value-generating opportunities to the user device via a content channel between the server and the user device.

Abstract: Provided are systems and methods for improving understanding and access of unstructured text-based web postings. In one example, a method may include receiving a web posting comprising unstructured text, identifying one or more explicit attributes of an activity based on text expressly recited in the unstructured text, determining one or more hidden attributes of the activity not recited in the unstructured text based on a pattern of words included in the unstructured text, converting the unstructured text into a structured object format comprising the one or more explicit attributes and the one or more determined hidden attributes, and outputting the web posting with the structured object format for display via a user interface.

Abstract: Provided are systems and methods for improving understanding and access of unstructured text-based web postings. In one example, a method may include receiving a web posting comprising unstructured text, identifying one or more explicit attributes of an activity based on text expressly recited in the unstructured text, determining one or more hidden attributes of the activity not recited in the unstructured text based on a pattern of words included in the unstructured text, converting the unstructured text into a structured object format comprising the one or more explicit attributes and the one or more determined hidden attributes, and outputting the web posting with the structured object format for display via a user interface.