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 includes a network classification system (NCS) that generates a set of machine learning (ML) features from information about information objects accessed by various users, and determines an organization (org) type associated with the network address based on the set of ML features. Obtained network events may include the information about the accessed information objects. A content consumption monitor (CCM) generates consumption scores for the network addresses based on the identified org types. The CCM can generate more accurate intent and consumption data by filtering out events unrelated to content consumption for that org type. The NCS and the CCM may be implemented as the same network function, or the NCS and CCM may be implemented as separate network functions. 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.

Abstract: A content consumption monitor (CCM) receives events identifying how and when users access content. The CCM may identify internet protocol (IP) addresses associated with the events and determine types of establishments associated with the IP addresses based on how the users access the content at the IP addresses. The CCM may generate consumption scores for the IP addresses based on the identified types of establishments. For example, the CCM can generate consumption scores for IP addresses associated with private business locations. The CCM can generate more accurate intent and consumption data by filtering out events unrelated to company content consumption.

Abstract: An event processor identifies events generated by an entity from a hostname website cluster and other third party websites. The event processor may generate a website cluster interest score based on the events indicating an interest level of the entity in multiple hostname websites, belonging to first party. The event processor also may identify a topic cluster including multiple topics and generate a topic cluster interest score indicating an interest level of the entity in the topics. The event processor may generate a buyer intent score based on the website interest score and the topic cluster interest score. The buyer intent score may provide a good indication of when the entity is interested in buying items from the party associated with the hostname website.