Abstract: Provided is a computer-implemented method for generating synthetic graphs that simulate real-time payment transactions that includes generating a base payment graph includes a plurality of nodes and a plurality of edges connecting the plurality of nodes, wherein each node represents an entity and each edge represents a probability that a real-time-payment transaction may be conducted involving two entities that are connected by the edge, wherein the real-time payment transaction is artificially created, generating a plurality of dynamic payment graphs based on the base payment graph, inserting patterns representing adversarial activity into the plurality of dynamic payment graphs, and performing an action associated with a machine learning technique using the plurality of dynamic payment graphs. Systems and computer program products are also provided.

Abstract: Apparatuses, methods, and systems are provided for making sequential recommendations using transition regularized non-negative matrix factorization. A non-application specific collaborative filtering based personalized recommender system can recommend a next logical item from a series of related items to a user. The recommender system can recommend a next desirable or series of next desirable new items to the user based on the historical sequence of all user-item preferences and a user's most recent interaction with an item. An asymmetric item-to-item transition matrix can capture aggregate sequential user-item interactions to design a loss function for matrix factorization that incorporates the transition information during decomposition into low-rank factor matrices.

Abstract: Provided are systems, methods, and computer program products for generating node embeddings. The system includes at least one processor programmed or configured to generate a graph comprising a plurality of nodes, generate an embedding for each node of the plurality of nodes, each embedding comprising at least one polar angle and a vector length. store each embedding of a plurality of embeddings in memory, and in response to processing the graph with a machine-learning algorithm, convert at least one embedding of the plurality of embeddings to Cartesian coordinates.

Abstract: Described are a system, method, and computer program product for applying deep learning analysis to predict and automatically respond to predicted changes in financial device primacy for a financial device holder. The method includes receiving transaction data representative of a plurality of transactions between the financial device holder and at least one merchant. The method also includes generating time series data based on the transaction data and generating a predictive model configured to: (i) receive an input of time-interval-based transaction data; and (ii) output a probability of primary financial device primacy change, the predictive model trained based on historic transaction data. The method further includes determining a probability of primary financial device primacy change for the financial device holder by applying the predictive model to the time series data. The method further includes, generating at least one communication to at least one issuer and/or the financial device holder.

Abstract: Described are a system, method, and computer program product for applying deep learning analysis to predict and automatically respond to predicted changes in financial device primacy for a financial device holder. The method includes receiving transaction data representative of a plurality of transactions between the financial device holder and a merchant. The method also includes generating time series data based on the transaction data and generating a predictive model configured to: (i) receive an input of time-interval-based transaction data; and (ii) output a probability of primary financial device primacy change, the predictive model trained based on historic transaction data. The method further includes determining a probability of primary financial device primacy change for the financial device holder by applying the predictive model to the time series data. The method further includes, generating at least one communication to at least one issuer and/or the financial device holder.

Abstract: A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.

Abstract: Provided are computer-implemented methods for generating embeddings for objects which may include receiving heterogeneous network data associated with a plurality of objects in a heterogeneous network; selecting at least one pattern of objects; determining instances of each pattern of objects based on the heterogeneous network data; generating a pattern matrix for each pattern of objects based on the instances of the pattern of objects; generating pattern sequence data associated with a portion of each pattern matrix; generating network sequence data associated with a portion of the heterogeneous network data; and combining the pattern sequence data and the network sequence data into combined sequence data. In some non-limiting embodiments or aspects, methods may include generating a vector for each object of the plurality of objects based on the combined sequence data. Systems and computer program products are also provided.

Abstract: Recommending analytic tasks based on similarity of datasets is disclosed. One example is a system including a data processor, a matching module, and a recommendation module. The data processor receives an incoming dataset via a processing system, and generates a feature vector for the incoming dataset. The matching module determines similarity measures between the generated feature vector and representative feature vectors for a plurality of datasets in a data repository, and selects at least one dataset of the plurality of datasets based on the similarity measures. The recommendation module identifies at least one analytic task associated with the selected dataset, and recommends, to a computing device via the processing system, the at least one analytic task to be performed on the incoming dataset.

Abstract: Apparatuses, methods, and systems are provided for making sequential recommendations using transition regularized non-negative matrix factorization. A non-application specific collaborative filtering based personalized recommender system can recommend a next logical item from a series of related items to a user. The recommender system can recommend a next desirable or series of next desirable new items to the user based on the historical sequence of all user-item preferences and a user's most recent interaction with an item. An asymmetric item-to-item transition matrix can capture aggregate sequential user-item interactions to design a loss function for matrix factorization that incorporates the transition information during decomposition into low-rank factor matrices.

Abstract: Methods, systems, and computer program products for knowledge graph based embedding, explainability, and/or multi-task learning may connect task-specific inductive models with knowledge graph completion and enrichment processes.

Abstract: Apparatuses, methods, and systems are provided for making sequential recommendations using transition regularized non-negative matrix factorization. A non-application specific collaborative filtering based personalized recommender system can recommend a next logical item from a series of related items to a user. The recommender system can recommend a next desirable or series of next desirable new items to the user based on the historical sequence of all user-item preferences and a user's most recent interaction with an item. An asymmetric item-to-item transition matrix can capture aggregate sequential user-item interactions to design a loss function for matrix factorization that incorporates the transition information during decomposition into low-rank factor matrices.

Abstract: Apparatuses, methods, and systems are provided for making sequential recommendations using transition regularized non-negative matrix factorization. A non-application specific collaborative filtering based personalized recommender system can recommend a next logical item from a series of related items to a user. The recommender system can recommend a next desireable or series of next desirable new items to the user based on the historical sequence of all user-item preferences and a user's most recent interaction with an item. An asymmetric item-to-item transition matrix can capture aggregate sequential user-item interactions to design a loss function for matrix factorization that incorporates the transition information during decomposition into low-rank factor matrices.

Abstract: Examples for mapping a relational database to a graph database include a mapping engine to execute an arbitrary query on a relational database, identify a result column tag based on a tag of an underlying base table, process the result column into a typed, directed property graph based on the result column tag, and output the typed, directed property graph to a graph database. Examples for mapping a graph database to a relational database include processing a graph transaction by updating a mapping layer with a surrogate describing a change to a database object, determining, for an object in the mapping layer, if a database constraint defined on the object is satisfied, collecting database changes defined by the surrogate into a database change request, submitting the change request to a relational database as a transaction, and deleting the surrogate for the object in the mapping layer.

Abstract: Systems, apparatuses, methods, and computer-readable media are provided to alleviate data sparsity in cross-recommendation systems. In particular, some embodiments are directed to a recommendation framework that addresses data sparsity and data scalability challenges seamlessly by meta-transfer learning contextual invariances cross domain, e.g., from dense source domain to sparse target domain. Other embodiments may be described and/or claimed.

Abstract: Examples for graph database query classification include receiving a graph query and determining if the graph query matches benchmark data. In the event that the graph query does not match benchmark data, the query may be parsed, a canonical internal representation of the query may be determined, the representation may be mapped to a rule, and the query may be classified based on the rule. In the event that the confidence score for the query classification does not exceed a threshold, the query may be sent to a synthetic graph or synopsis for simulation. In some examples, the simulation may include selecting computationally expensive graph operators in the query for simulation.

Abstract: Described are a system, method, and computer program product for applying deep learning analysis to predict and automatically respond to predicted changes in financial device primacy for a financial device holder. The method includes receiving transaction data representative of a plurality of transactions between the financial device holder and a merchant. The method also includes generating time series data based on the transaction data and generating a predictive model configured to: (i) receive an input of time-interval-based transaction data; and (ii) output a probability of primary financial device primacy change, the predictive model trained based on historic transaction data. The method further includes determining a probability of primary financial device primacy change for the financial device holder by applying the predictive model to the time series data. The method further includes, generating at least one communication to at least one issuer and/or the financial device holder.

Abstract: Provided is a system, method, and computer program product for predicting a specified geographic area of a user. The method includes receiving transaction data associated with a plurality of transactions during a predetermined time interval. The method also includes generating a geographic area prediction model based on the transaction data by determining a verified geographic area for each user, and determining transaction data associated with a plurality of transactions involving each user for a plurality of feature vector parameters, training the geographic area prediction model based on the plurality of feature vector parameters for the verified geographic area for each user, and validating the geographic area prediction model based on the plurality of feature vector parameters for the verified geographic area for each user.

Abstract: Examples for mapping a relational database to a graph database include a mapping engine to execute an arbitrary query on a relational database, identify a result column tag based on a tag of an underlying base table, process the result column into a typed, directed property graph based on the result column tag, and output the typed, directed property graph to a graph database. Examples for mapping a graph database to a relational database include processing a graph transaction by updating a mapping layer with a surrogate describing a change to a database object, determining, for an object in the mapping layer, if a database constraint defined on the object is satisfied, collecting database changes defined by the surrogate into a database change request, submitting the change request to a relational database as a transaction, and deleting the surrogate for the object in the mapping layer.

Abstract: A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.

Abstract: Examples for graph database query classification include receiving a graph query and determining if the graph query matches benchmark data. In the event that the graph query does not match benchmark data, the query may be parsed, a canonical internal representation of the query may be determined, the representation may be mapped to a rule, and the query may be classified based on the rule. In the event that the confidence score for the query classification does not exceed a threshold, the query may be sent to a synthetic graph or synopsis for simulation. In some examples, the simulation may include selecting computationally expensive graph operators in the query for simulation.