Abstract: An IP-to-Domain (IP2D) resolution system predicts which domain is most likely associated with an IP address. The resolution system generates unique source vote features (FSV) from (IP, domain, source) data. The FSV features are used to train a machine learning model that predicts which domain is most likely associated with an IP address. The domain predictions can then be used to more efficiently process events, more accurately calculate consumption scores, and more accurately detect associated company surges.

Abstract: An IP-to-Domain (IP2D) resolution system predicts which domain is most likely associated with an IP address. The resolution system generates unique source vote features (FSV) from (IP, domain, source) data. The FSV features are used to train a computer learning model that predicts which domain is most likely associated with an IP address. The domain predictions can then be used to more efficiently process events, more accurately calculate consumption scores, and more accurately detect associated company surges.

Abstract: Disclosed embodiments include a resource classification system (RCS) identifies one or more features in information objects (InObs) and uses the features to classify the InObs. The features may be based on structural semantics of the InObs, content semantics of InObs, content interaction behavior with the InObs, types of users accessing the InObs, and/or the like. The RCS may generate vectors that represent the different features. The vectors may be used to train a machine learning model to predict resource classifications of the InObs. The predicted resource classifications provide more accurate intent, consumption, and surge score predictions than existing solutions. Other embodiments may be described and/or claimed.

Abstract: Disclosed embodiments include an event processor that identifies events generated by an entity from various resources. The event processor generates a resource cluster interest score based on the events indicating an interest level of the entity in multiple hostname resources belonging to a first party. The event processor identifies a topic cluster including multiple topics and generates a topic cluster interest score indicating an interest level of the entity in the topics. The event processor generates a weighted intent score based on the resource interest score and the topic cluster interest score. The weighted intent score provides an indication of when the entity is interested in consuming resources, or interested in products/services, provided by the first party. Other embodiments may be described and/or claimed.

Abstract: An IP-to-Domain (IP2D) resolution system predicts which domain is most likely associated with an IP address. The resolution system generates unique source vote features (FSV) from (IP, domain, source) data. The FSV features are used to train a computer learning model that predicts which domain is most likely associated with an IP address. The domain predictions can then be used to more efficiently process events, more accurately calculate consumption scores, and more accurately detect associated company surges.

Abstract: A website classification system identifies one or more features in websites and uses the features to classify the websites. The website classification system may generate features identifying structural semantics of webpages, content semantics of webpages, content interaction behavior with the webpages, or types of users accessing the webpages. The website classification system may generate vectors that represent the different features. A first set of vectors from classified websites are used for training a computer learning model. Vectors from unclassified websites are then fed into the trained learning model to predict a particular website classification. The predicted website classifications provide more accurate intent, consumption, and surge score predictions.