Abstract: Segment valuation techniques usable in a digital medium environment are described. To do so, a segment valuation system first identifies the attributes that are significant in achievement of a desired metric (e.g., conversion) and then values segments based on those significant attributes. Attributes are selected from the trained model based on significance of those attributes towards achieving the desired metric. A valuation of a segment may then be calculated based on the valuations of these attributes. For example, inclusion of the selected attributes within a segment, and the valuations of those selected attributes, is then used by the segment valuation system to generate data describing a value of the segment towards achieving the metric.

Abstract: Matrix completion and recommendation provision with deep learning is described. A matrix manager system imputes unknown values of incomplete input matrices using deep learning. Unlike conventional techniques, the matrix manager system completes incomplete input matrices using deep learning regardless of whether an input matrix represents numerical, categorical, or a combination of numerical and categorical attributes. To enable a machine-learning model (e.g., an autoencoder) to complete a matrix, the matrix manager system initially encodes the matrix. This involves normalizing known values of numerical attributes and categorically encoding known values of categorical attributes. The matrix manager system performs categorical encoding by replacing information of a given categorical attribute (e.g., an attribute column) with replacement information for each possible value of the attribute (e.g., new columns for each possible value).

Abstract: Segment valuation techniques usable in a digital medium environment are described. To do so, a segment valuation system first identifies the attributes that are significant in achievement of a desired metric (e.g., conversion) and then values segments based on those significant attributes. Attributes are selected from the trained model based on significance of those attributes towards achieving the desired metric. A valuation of a segment may then be calculated based on the valuations of these attributes. For example, inclusion of the selected attributes within a segment, and the valuations of those selected attributes, is then used by the segment valuation system to generate data describing a value of the segment towards achieving the metric.

Abstract: Segment valuation techniques usable in a digital medium environment are described. To do so, a segment valuation system first identifies the attributes that are significant in achievement of a desired metric (e.g., conversion) and then values segments based on those significant attributes. Attributes are selected from the trained model based on significance of those attributes towards achieving the desired metric. A valuation of a segment may then be calculated based on the valuations of these attributes. For example, inclusion of the selected attributes within a segment, and the valuations of those selected attributes, is then used by the segment valuation system to generate data describing a value of the segment towards achieving the metric.

Abstract: Matrix completion and recommendation provision with deep learning is described. A matrix manager system imputes unknown values of incomplete input matrices using deep learning. Unlike conventional techniques, the matrix manager system completes incomplete input matrices using deep learning regardless of whether an input matrix represents numerical, categorical, or a combination of numerical and categorical attributes. To enable a machine-learning model (e.g., an autoencoder) to complete a matrix, the matrix manager system initially encodes the matrix. This involves normalizing known values of numerical attributes and categorically encoding known values of categorical attributes. The matrix manager system performs categorical encoding by replacing information of a given categorical attribute (e.g., an attribute column) with replacement information for each possible value of the attribute (e.g., new columns for each possible value).

Abstract: Systems, methods, and non-transitory computer-readable media (systems) are disclosed for generating an analytics insight from a data set based on learning from a different data set. In particular, in one or more embodiments, the disclosed systems analyze a first data set to determine significant features related to an analytics metric. The disclosed systems determine a correlation between features of a second data set and the significant features of the first data set. Furthermore, in one or more embodiments, the disclosed systems utilize the correlation to generate an analytics insight, such as insights on segment of users. In one or more embodiments, the first data set and the second data set contain different features and/or different users and the second data set lacks data regarding the analytics metric.

Abstract: Segment valuation techniques usable in a digital medium environment are described. To do so, a segment valuation system first identifies the attributes that are significant in achievement of a desired metric (e.g., conversion) and then values segments based on those significant attributes. Attributes are selected from the trained model based on significance of those attributes towards achieving the desired metric. A valuation of a segment may then be calculated based on the valuations of these attributes. For example, inclusion of the selected attributes within a segment, and the valuations of those selected attributes, is then used by the segment valuation system to generate data describing a value of the segment towards achieving the metric.