Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

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 performs personalized transaction categorization. A transaction record is received, by a server application. In a first stage, sparse raw features are extracted from a transaction record of a transaction and converted into a transaction vector including dense features. In a second stage, the transaction vector is classified into a customized chart of accounts using the dense features to generate adapter model output. The method further includes selecting, an account identifier, corresponding to the transaction record and to an account of the customized chart of accounts, using the adapter model output, and presenting the account identifier for the transaction record.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: The present disclosure provides a composite machine learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine learning model is updated based on the descriptive string and the label. The machine learning model is then trained against the updated set of training data.

Abstract: The invention relates to a method for mapping topics. The method includes obtaining terms, obtaining tokens from each term, and identifying a first and a second set of topics. Each of the topics represents one or more of the terms. The method further includes identifying first and second topic names for the first and the second sets of topics. For each topic, the tokens associated with the terms assigned to the topic are analyzed for relevance, and a token with a high relevance is selected as the topic name. The method also includes selecting one of the first and one of the second sets of topics to obtain first and second selected topics, determining, based on the one or more terms, a similarity value between each of the first and the second selected topics, and establishing a mapping between similar first and second selected topics.

Abstract: The invention relates to a method for mapping topics. The method includes obtaining terms, obtaining tokens from each term, and identifying a first and a second set of topics. Each of the topics represents one or more of the terms. The method further includes identifying first and second topic names for the first and the second sets of topics. For each topic, the tokens associated with the terms assigned to the topic are analyzed for relevance, and a token with a high relevance is selected as the topic name. The method also includes selecting one of the first and one of the second sets of topics to obtain first and second selected topics, determining, based on the one or more terms, a similarity value between each of the first and the second selected topics, and establishing a mapping between similar first and second selected topics.

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: A method performs personalized transaction categorization. A transaction record is received, by a server application. In a first stage, sparse raw features are extracted from a transaction record of a transaction and converted into a transaction vector including dense features. In a second stage, the transaction vector is classified into a customized chart of accounts using the dense features to generate adapter model output. The method further includes selecting, an account identifier, corresponding to the transaction record and to an account of the customized chart of accounts, using the adapter model output, and presenting the account identifier for the transaction record.

Abstract: A method including obtaining terms that are specific to a domain. First and second sets of the terms are obtained from first and second users. The first set do not adhere to a standard; the second terms do adhere to the standard. Tokens are obtained from the terms. First and second topics, representing terms, are identified within the domain. The terms are assigned to exactly one corresponding topic. The terms are assigned to the topics. First and second topic names are identified for the first and second topics. Identifying includes analyzing, for relevance, ones of the tokens. Identifying also includes selecting a particular token as a selected topic name for a selected one of the first topics and the second topics. A similarity value is determined between the first and the second selected topics. A mapping is established, based on the similarity value, between the first and second selected topic.

Abstract: A method is disclosed. The method includes: obtaining a help request associated with a user operating an application and a click stream of the user within the application; generating a feature data structure based on the help request and the click stream; generating, by applying the feature data structure to a machine learning model, a topic data structure including a plurality of scores corresponding to a plurality of topics; obtaining a plurality of topic distributions corresponding to a plurality of articles; identifying, by applying the topic data structure to the plurality of topic distributions, a subset of the plurality of articles for the user; and displaying, in response to the help request, a graphical user interface (GUI) including a plurality of links to the subset of the plurality of articles.

Abstract: Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time-stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.

Abstract: Certain aspects of the present disclosure provide techniques for determining content quality of a set of content item based on generating a score for each content item. An example technique includes using a trained content quality model to generate the score for each content item component. In such example, the model is trained using a calculated set of features associated with text and metadata of a content item in a training data set as well as a quality label for the content item. The model is trained by associating the set of features with the quality label for each content item in the training data.

Abstract: The present disclosure provides a composite machine learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine learning model is updated based on the descriptive string and the label. The machine learning model is then trained against the updated set of training data.

Abstract: The present disclosure provides a composite machine-learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine-learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine-learning model is updated based on the descriptive string and the label. The machine-learning model is then trained against the updated set of training data.

Abstract: The invention relates to a method for generating a topic name for accounts grouped under a topic. The method includes obtaining account names associated with the accounts, generating a plurality of n-grams from the account names, and for each n-gram, obtaining a quality score based on relevance and meaningfulness of the n-gram. The relevance is determined using at least one relevance score that reflects how representative the n-gram is for the plurality of n-grams, and the meaningfulness is determined based on whether the n-gram exists in a knowledge base. The method further includes assigning one or more of the n-grams to the topic as the topic name, based on the quality score generated for each of the n-grams.

Abstract: A method including obtaining terms that are specific to a domain. First and second sets of the terms are obtained from first and second users. The first set do not adhere to a standard; the second terms do adhere to the standard. Tokens are obtained from the terms. First and second topics, representing terms, are identified within the domain. The terms are assigned to exactly one corresponding topic. The terms are assigned to the topics. First and second topic names are identified for the first and second topics. Identifying includes analyzing, for relevance, ones of the tokens. Identifying also includes selecting a particular token as a selected topic name for a selected one of the first topics and the second topics. A similarity value is determined between the first and the second selected topics. A mapping is established, based on the similarity value, between the first and second selected topic.

Abstract: A processor may identify a combination term including at least two individual terms within at least one source of truth stored in a memory in communication with the processor. The processor may identify at least one document including the at least two of the individual search terms. The processor may determine a document weight for the at least one document based on the combination search term and the at least two of the individual search terms within the combination search term. The processor may provide the document as a search result arranged according to the document weight and/or may index the document according to the document weight.