Abstract: To improve processing of the multi-event time-series data, information about each event type is aggregated for a group of time bins, such that an event bin embedding represents the occurring events of that type in the time bin. The event bin embedding may be based on an aggregated event value summarizing the values of that event type in the bin and a count of those events. The event bin embeddings across event types and time bins may be combined with an embedding for static data about the data instance and a representation token for input to an encoder. The encoder may apply an event-focused sublayer and a time-focused sublayer that attend to respective dimensions of the encoder. The model may be initially trained with self-supervised learning with time and event masking and then fine-tuned for particular applications.

Abstract: A model evaluation system evaluates the effect of a feature value at a particular time in a time-series data record on predictions made by a time-series model. The time-series model may make predictions with black-box parameters that can impede explainability of the relationship between predictions for a data record and the values of the data record. To determine the relative importance of a feature occurring at a time and evaluated at an evaluation time, the model predictions are determined on the unmasked data record at the evaluation time and on the data record with feature values masked within a window between the time and the evaluation time, permitting comparison of the evaluation with the features and without the features. In addition, the contribution at the initial time in the window may be determined by comparing the score with another score determined by masking the values except for the initial time.

Abstract: In some examples, computer-implemented systems and processes facilitate a prediction of occurrences of future events using trained artificial intelligence processes and normalized feature data. For instance, an apparatus may generate an input dataset based on elements of interaction data that characterize an occurrence of a first event during a first temporal interval, and that include at least one element of normalized data. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of a second event associated with during a second temporal interval. The apparatus may also transmit at least a portion of the output data to a computing system, which may perform operations consistent with the portion of the output data.

Abstract: The disclosed embodiments include computer-implemented systems and methods that facilitate a prediction of future occurrences of redemption events using adaptively trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of each of a plurality of targeted events during a second temporal interval. The apparatus may also transmit at least a portion of the output data and explainability data associated with the trained artificial intelligence process to a computing system, which may perform operations based on the portion of the output data and the explainability data.

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of targeted classes of events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on interaction data associated with a prior temporal interval, and may apply a trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of an expected occurrence of a corresponding one of a plurality of targeted events during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval.

Abstract: A recommendation system generates recommendations for user-item pairs based on embeddings in hyperbolic space. Each user and item may be associated with a local hyperbolic embedding representing the user or item in hyperbolic space. The hyperbolic embedding may be modified by neighborhood information. Because the hyperbolic space may have no closed form for combining neighbor information, the local embedding may be converted to a tangent space for neighborhood aggregation information and converted back to hyperbolic space for a neighborhood-aware embedding to be used in the recommendation score.

Abstract: The disclosed embodiments include computer-implemented apparatuses and methods that predict occurrences of temporally separated events using adaptively trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on first interaction data that characterizes an occurrence of a first event, and may apply a trained artificial intelligence process to the input dataset. Based on the application of the trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of a second event within a predetermined time period subsequent to the occurrence of the first event, and may transmit the output data to a computing system. The computing system may generate second interaction data specifying an operation associated with the occurrence of the first event based on the output data, and perform the operation in accordance with the second interaction data.