Abstract: A rating prediction engine builds and applies models to predict ratings based on an analysis of textual reviews and comments. The engine can build multiple models simultaneously through distributed parallel model building that employs deep convolutional neural networks (CNNs). The engine can also incorporate user moment feature data, including user status and context information, to provide better performance and more accurate predictions. The engine can also employ heuristic unsupervised pre-training and/or adaptive over-fitting reduction for model building. In some instances, the techniques described herein can be used in a service to predict personalized ratings for reviews or other published items, in instances where the original author of the item did not include a rating and/or in instances where the publication channel does not provide a mechanism to enter ratings.

Abstract: A rating prediction engine builds and applies models to predict ratings based on an analysis of textual reviews and comments. The engine can build multiple models simultaneously through distributed parallel model building that employs deep convolutional neural networks (CNNs). The engine can also incorporate user moment feature data, including user status and context information, to provide better performance and more accurate predictions. The engine can also employ heuristic unsupervised pre-training and/or adaptive over-fitting reduction for model building. In some instances, the techniques described herein can be used in a service to predict personalized ratings for reviews or other published items, in instances where the original author of the item did not include a rating and/or in instances where the publication channel does not provide a mechanism to enter ratings.

Abstract: In some implementations, a computer-implemented method for generating computer-readable data models includes receiving time series data; applying a plurality of variable transformations to the time series data to generate a variable matrix with first and second dimensions; partitioning the variable matrix along a first one of the first and second dimensions to generate a plurality of data sets; partitioning the plurality of data sets along a second one of the first and second dimensions to generate a plurality data subsets; providing each of the plurality of data subsets to a respective computational unit in a distributed computing environment for evaluation; receiving, from the respective computational units, scores for a plurality of variables as determined by the respective computational units from the plurality of data subsets; and selecting a portion of the plurality of variables as having at least a threshold level of accuracy in modeling the time series data.

Abstract: In some implementations, a computer-implemented method for generating computer-readable data models includes receiving time series data; applying a plurality of variable transformations to the time series data to generate a variable matrix with first and second dimensions; partitioning the variable matrix along a first one of the first and second dimensions to generate a plurality of data sets; partitioning the plurality of data sets along a second one of the first and second dimensions to generate a plurality data subsets; providing each of the plurality of data subsets to a respective computational unit in a distributed computing environment for evaluation; receiving, from the respective computational units, scores for a plurality of variables as determined by the respective computational units from the plurality of data subsets; and selecting a portion of the plurality of variables as having at least a threshold level of accuracy in modeling the time series data.