Abstract: At an artificial intelligence-based service, an indication of a similarity group of items of a data stream is obtained. A subset of the stream items is to be included in an ordered collection and presented via an interface which allows one or more types of interactions. Using a first data set which includes interaction records of items in the similarity group, one or more machine learning models are trained to predict a relevance metric associated with a particular type of interaction. A predicted value of the relevance metric is obtained from a trained version of a model and stored.

Abstract: The effect of intent bias on content performance can be determined in order to provide more relevant content in response to a query or other opportunity. Performance data can include the frequency with which an action, such as a purchase, occurs in response to an instance of the content being displayed. An intent bias model can be trained using the performance data for two or more intents, such as an action intent and an explore intent. Once the intent bias for an offer is determined, a normalized performance value can be obtained that does not include the effects of the bias. The normalized values can be used to select and place content based on actual performance.

Abstract: The effect of position bias on content performance can be determined in part using artificial intelligence and computer learning. Performance data can include the frequency with which an action, such as a purchase, occurs in response to an instance of content being displayed. The content can be associated with a node at a lowest level of an offering hierarchy, and performance data from the various levels can be rolled up to higher level nodes to obtain sufficient data to for accurate position bias determinations. A bias model can be trained using the data from the various levels, where the training determines weightings for the bias determinations of each level. Once the position bias for an offer is determined, a normalized performance value can be obtained that does not include the effects of the bias. The normalized values can be used to select and place content based on actual performance.

Abstract: A method for profiling network traffic of a network. The method includes obtaining a signature library comprising a plurality of signatures corresponding to a plurality of behavioral models, generating, based on a first pre-determined criterion, a group behavioral model associated with the signature library, wherein the group behavioral model represents a common behavior of a plurality of historical flows identified from the network traffic, wherein each of the plurality of signatures correlates to a subset of the plurality of historical flows, selecting a flow in the network traffic for including in a target flow set, wherein the flow matches the group behavioral model without matching any of the plurality of behavioral models, analyzing the target flow set to generate a new signature, and adding the new signature to the signature library. Further, each behavioral model is generated from a kernel constructed using boosting of decision tree learning methods.