Abstract: Technologies for generating relevance-independent position effects estimates for a set of ranked digital items are described. Embodiments include creating an input data set that includes request tracking data and associated activity tracking data. A relevance-independent position effects estimator generates an output data set. An item of the output data set includes user interface position data associated with a pair of adjacently positioned items of the input data set. The user interface position data indicates that a change in user interface activity probability data relating to a change in position between the items of the pair is greater than a change in the user interface activity probability data relating to a difference in the relevance score between the items of the pair. The output data set is stored in a searchable data store. Data from the searchable data store is provided to a downstream service.

Abstract: Technologies for generating relevance-independent position effects estimates for a set of ranked digital items are described. Embodiments include creating an input data set that includes request tracking data and associated activity tracking data. A relevance-independent position effects estimator generates an output data set. An item of the output data set includes user interface position data associated with a pair of adjacently positioned items of the input data set. The user interface position data indicates that a change in user interface activity probability data relating to a change in position between the items of the pair is greater than a change in the user interface activity probability data relating to a difference in the relevance score between the items of the pair. The output data set is stored in a searchable data store. Data from the searchable data store is provided to a downstream service.

Abstract: In an example embodiment, a machine-learned model is trained to forecast a performance-based metric for a piece of content based on a budget applied to the piece of content. The increase in the performance-based metric that is due to a corresponding increase in budget may be termed “incrementality.” The machine-learned model is trained in such a way that incrementality is built into the model. More particularly, in an example embodiment, an asymmetric budget split process is used to create two groups of training data, one for high budget and one for low budget. Rather than relying on historical data, the asymmetric budget split process applies a high budget to a piece of content in a first subgroup (e.g., group of users) and a low budget to that same piece of content in a second subgroup, and then the performance results in each subgroup are compared.