Abstract: In an example method, a system receives a first data set representing a plurality of first purchases of a plurality of first products by a plurality of first users, and trains a generative transformer model including one or more computerized attention mechanisms using the first data set as an input. Further, the system receives a second data set representing one or more second products selected by a second user for purchase, and provides the second data set to the generative transformer model. The system outputs a third data set generated by the generative transformer model based on the second data set, where the third data set represents a prediction of one or more third products for purchase by the second user, and stores the third data set using one or more computer storage devices.

Abstract: Methods, systems, and computer storage media for providing a dynamically weighted unobserved component model (“DW-UCM”) in a demand forecasting engine of a data analytics system. Dynamic weighting is performed based on a machine learning framework that includes tools, interfaces, and a library for developing improved machine learning models (e.g., dynamic demand forecasting models) of a dynamic weighting machine learning pipeline. In particular, the dynamic weighting machine learning pipeline can include a first module that is configured to predict if a segment (e.g., travel segment) under evaluation is open or closed (e.g., due to a restriction or rule), a second module that forecasts near-term recovery (e.g., approx. 0 - 4 weeks), and a third module that predicts longer term recovery.

Abstract: Methods and systems of using reinforcement learning to optimizing promotions. A promotion can be offered to a user using a reinforcement learning model with a sensitivity parameter, the reinforcement module estimating a time period during which the user will respond to the first information. The user's reaction to the promotion can be observed. The reinforcement learning model can be adapted based on the user's reaction.

Abstract: Methods and systems of using reinforcement learning for promotions. A first promotion is offered to a customer for a product and/or service. A first reward or penalty is determined, via a reinforcement machine learning model, based on the customer's reaction to the first promotion, wherein the reinforcement machine learning model is at a first state. Feedback to the reinforcement machine learning model is provided based on the first reward or penalty. A state of the reinforcement machine learning model is changed, based on the feedback, from the first state to a second state.

Abstract: Methods and Systems for using reinforcement learning to optimize promotions. A promotion can be offered to a customer for a prepaid calling card using a reinforcement learning model with a sensitivity parameter. The reinforcement learning model can estimate a time period during which the customer will purchase the prepaid calling card. The customer's reaction to the promotion can be observed. A reward or a penalty can be collected based on the customer's reaction. The reinforcement learning model can be adapted based on the reward or the penalty to optimize the timing of the promotion by estimating a new time period during which the customer will purchase the prepaid calling card. The reward proxy and/or the penalty proxy can comprise frequency of usage.

Abstract: Methods and systems for using reinforcement learning to optimizing promotions. A promotion for a product and/or service is offered to a customer using a reinforcement learning model. The customer's reaction is observed. A reward or a penalty is collected based on the customer's reaction. The reinforcement learning model is adapted based on the reward or penalty.

Abstract: Methods and systems for predicting are disclosed. Records for transactions can be stored, each record comprising an indication identifying items involved in a transaction and an indication of a time elapsed between the transaction and a previous transaction. The records for transactions can be analyzed to produce a probability that the items will be involved in a next transaction.

Abstract: Systems and methods for predicting are described herein. A record for each of a plurality of events associated with user transactions can be stored. A sequential plurality of the events can be analyzed using a unidirectional long short term memory (LSTM) and first and second dense neural network layers configured to receive output from the LSTM network.

Abstract: A processor circuit may perform processing associated with storing in a memory a record for each of a plurality of transactions, each record comprising an indication identifying at least one item, of a set of items, involved in the transaction and an indication of a time elapsed between the transaction and a previous transaction. The processor circuit may perform processing associated with analyzing a sequential plurality of the transactions using at least a long short term memory (LSTM) network to produce a probability that the at least one item will be involved in a next transaction for each at least one item, and a time estimate until the next transaction occurs. The processor circuit may perform processing associated with storing each probability and the time estimate in the memory as a prediction.

Abstract: Systems and methods for predicting transactions. A record is stored for each of a plurality of transactions, each record comprising an indication identifying each item from a set of items involved in the transaction and an indication of a time elapsed between the transaction and a previous transaction. A sequential plurality of the transactions is analyzed using a unidirectional long short term memory (LSTM) network to produce a probability that each item from the set of items will be involved in a next transaction, and a time estimate value for a future time when the next transaction will occur. The probability for each item and the time estimate value is stored in the memory as a prediction.