Abstract: Techniques for optimizing content item delivery for installations or activations of a mobile application are provided. In one technique, a machine-learned model is trained based on multiple training instances that individually indicate whether an entity performed a particular action relative to a mobile application. In response to receiving a content item request from a third-party content delivery exchange, it is determined whether a client device that initiated the content item request has activated a particular application. In response to determining that the client device has not activated the particular application, multiple feature values of the content item request are identified. Based on inputting the feature values into the model, a score is generated that indicates a likelihood that an entity of the client device will perform the particular action relative to the particular application. Based on the score, a content item is transmitted over a network to the client device.

Abstract: Herein are techniques for content delivery pacing based on multidimensional forecasting. In an embodiment, a computer receives, for a content delivery campaign, targeting criteria and a resource usage limit of a limited resource. Entities that match the targeting criteria are identified for which content of the delivery campaign may have increased relevance. For each matching entity, a forecast of requests that might originate from the entity during each of a series of time intervals is generated to predict opportunities to deliver the content of the campaign. The forecasts of the matching entities can be combined to generate a combined forecast of requests for the targeting criteria. The computer generates, based on the combined forecast and the resource usage limit for the content delivery campaign, and stores for future use a fulfilment schedule that specifies amounts of requests to fulfill during the series of time intervals.

Abstract: Techniques for optimizing content item delivery for installations or activations of a mobile application are provided. In one technique, a machine-learned model is trained based on multiple training instances that individually indicate whether an entity performed a particular action relative to a mobile application. In response to receiving a content item request from a third-party content delivery exchange, it is determined whether a client device that initiated the content item request has activated a particular application. In response to determining that the client device has not activated the particular application, multiple feature values of the content item request are identified. Based on inputting the feature values into the model, a score is generated that indicates a likelihood that an entity of the client device will perform the particular action relative to the particular application. Based on the score, a content item is transmitted over a network to the client device.