Abstract: A method includes obtaining, from a matching model, for a user input, a set of candidate matches generated using a corresponding set of features. The corresponding set of features is partitioned into a first feature subset and a second feature subset. For each candidate match in the set of candidate matches, a candidate feature subset of features is individually generated. The candidate feature subset of features are selected from the first feature subset and the second feature subset for the candidate match. A multitude of candidate feature subsets corresponding to the set of candidate matches is obtained. The multitude of candidate feature subsets is clustered to obtain a first feature pattern and a second feature pattern. A large language model (LLM) generates a suggestion to modify the user input by processing the first feature pattern and the second feature pattern. The method further includes presenting the suggestion of the LLM.

Abstract: A method for re-training a machine learning model includes generating an embedding of an item of data associated with a user of a software application; storing the embedding of the item of data prior to a trigger event comprising a user action with respect to the item of data; obtaining data indicative of the trigger event during an online session for the user of the software application; retrieving the embedding of the item of data from the data store in response to obtaining the data indicative of the trigger event during the online session; generating updated training data for the machine learning model based, at least in part, on the embedding of the item of data and the user action with respect to the item of data; and providing the updated training data to a re-training algorithm configured to re-train the machine learning model in real-time model.

Abstract: A transaction model of a general model generates a target transaction vector for a target transaction record. The general model also generates account vectors for accounts. A match score is generated between the account vectors and the transaction vector. The general model selects a first account identifier of an account using the match score. The transaction model also generates historical transaction vectors for historical transaction records. Further, a comparison score is generated between the historical transaction vectors and the target transaction vector. A second account identifier of an historical transaction is selected according to the comparison score. One of the first account identifier and the second account identifier is selected as the account identifier for the transaction record, and the transaction record is stored with the account identifier.

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: 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: 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.