Abstract: Methods, apparatus, and computer-readable media are provided for analyzing a cluster of communications, such as B2C emails, to generate a template for the cluster that defines transient segments and fixed segments of the cluster of communications. More particularly, methods, apparatus, and computer-readable media are provided for generating and/or applying a trained structured machine learning model for a generated template that can be used to determine, for one or more transient segments of subsequent communications, a corresponding probability that a given semantic label is the correct semantic label for extracted content of the transient segment(s).

Abstract: In various embodiments, an optimization engine regenerates items included in an interactive page while the user is interacting with the interactive page. In operation, an optimization engine displays a portion of the interactive page during a viewing session. Subsequently, the optimization engine computes a probability distribution for the viewing session over a set of interests based on model parameters and operations performed by the user during the viewing session. The optimization engine then regenerates items that are included in a second portion of the interactive page based on the probability distribution for the viewing session. The optimization engine displays a least a part of the resulting regenerated interactive page. Advantageously, by regenerating items included in the interactive page based on operations performed by the user during the viewing session, the optimization engine reduces the time required for the user to view an item that piques an interest.