Abstract: A method utilizes a framework for transaction categorization personalization. A transaction record is received. a baseline model is selected from a plurality of machine learning models. An account identifier, corresponding to the transaction record using the baseline model, is selected. The account identifier for the transaction record is presented.

Abstract: A method categorizes transaction records. A transaction record is received by a server application. The transaction record is encoded with a first machine learning model to obtain a transaction vector, wherein the transaction vector is in a same vector space as multiple account vectors. A second machine learning model executing in the server application, selects an account vector, from the multiple account vectors, corresponding to the transaction vector. An account identifier, corresponding to the account vector, is presented for the transaction record.

Abstract: Certain aspects of the present disclosure provide techniques for improving a user experience based on electronic records of transactions. Embodiments include training a classifier using training data comprising a set of historical transaction descriptions and a set of corresponding historical classifications that indicate whether or not each historical transaction description of the set of historical transaction descriptions is associated with a user location. Embodiments further include receiving a transaction record describing a transaction associated with a user. Embodiments further include using the classifier to determine a classification for the transaction. The classification indicates whether or not the transaction description is associated with a location of the user. Embodiments further include providing, based at least in part on the classification, a communication to the user that relates to the location of the user.