Abstract: Techniques for leveraging existing statistical prediction models are provided. A first statistical prediction model is generated for a content item. An instruction is received to create a clone from the content item. In response to receiving the instruction, the clone is created based on attributes of the content item. A second statistical prediction model that is different than the first statistical prediction model is generated for the clone. In response to receiving a request for content, the clone is identified as relevant to the first request. A similarity between (1) first content of the content item and (2) second content of the clone is determined. If the similarity exceeds a similarity threshold, then the first statistical prediction model is used to generate a prediction of an entity user selection rate associated with the clone. Otherwise, the second statistical prediction model is used to generate the prediction.

Abstract: Techniques are provided for server-to-server interaction for content item selection. A system receives a request for one or more content items from a client device. In response to receiving the request, the system generates multiple content requests. Each content request is sent to a different content provider of multiple content providers. At least one of the content requests is transmitted over a network to a content provider that is remote relative to the system. The system receives multiple responses, each of which is from a different content provider of the multiple content providers. Each response identifies a different content item. The system makes a determination that at least one content item is associated with a particular web domain. The system selects, based on the determination, a particular content item of the plurality of content items and causes the particular content item to be transmitted to the client device.

Abstract: The techniques herein include an exception handler determining whether filtering criteria have been met for providing notification of an exception generated by a data ingest component in a data pipeline system to an exception analyzer. In response to determining that the filtering criteria is satisfied, the notification is provided to an exception analyzer. The exception analyzer analyzes the exception and selects a first reaction for an exception remediator to perform to attempt to recover from the exception based on the analysis. The chosen reaction may include automatically rolling back the data ingest component to a prior known stable software version, fixing source data ingested by the data ingest component, creating a troubleshooting ticket in a troubleshooting ticketing system, sending an electronic message to troubleshoot personnel, and the like. The reaction is then performed.

Abstract: Techniques for centralizing client-side logic for retrieving remote content items are provided. In one technique, a client device receives, from a server, data that includes first code and a web document. The data is processed at the client device, which processing comprises analyzing the web document to identify a first component that specifies multiple data items and executing code associated with the first component to generate a first reference that is based on the multiple data items, generate a first executable element, and insert the first reference into the first executable element. The first executable element is executed at the client device, wherein executing the first executable element causes a first request that includes the first reference to be transmitted over a network. In response to the first request, a first content item is received and displayed within a web page that is rendered based on the web document.

Abstract: Techniques are provided for server-to-server interaction for content item selection. A system receives a request for one or more content items from a client device. In response to receiving the request, the system generates multiple content requests. Each content request is sent to a different content provider of multiple content providers. At least one of the content requests is transmitted over a network to a content provider that is remote relative to the system. The system receives multiple responses, each of which is from a different content provider of the multiple content providers. Each response identifies a different content item. The system makes a determination that at least one content item is associated with a particular web domain. The system selects, based on the determination, a particular content item of the plurality of content items and causes the particular content item to be transmitted to the client device.

Abstract: Techniques for leveraging existing statistical prediction models are provided. A first statistical prediction model is generated for a content item. An instruction is received to create a clone from the content item. In response to receiving the instruction, the clone is created based on attributes of the content item. A second statistical prediction model that is different than the first statistical prediction model is generated for the clone. In response to receiving a request for content, the clone is identified as relevant to the first request. A similarity between (1) first content of the content item and (2) second content of the clone is determined. If the similarity exceeds a similarity threshold, then the first statistical prediction model is used to generate a prediction of an entity user selection rate associated with the clone. Otherwise, the second statistical prediction model is used to generate the prediction.