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: The present disclosure describes a content consumption monitor (CCM) that determines surges in content consumption based on changes in content consumptions scores. The CCM determines the content consumptions scores for domains and/or organizations (orgs) based on session events generated by different devices/users from the org and/or domain, a number of events generated by the org/domain, content and/or user interactions with the content indicated by the events, relevancy scores of the content to one or more topics, and/or other criteria. The CCM detects surges in consumption or interest in a topic for the domain/org when the consumption score reaches a threshold and/or within a period of time. The CCM may adjust the consumption score based on the changes in the relevancy, number of events and/or the number of users over different time periods. 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: Disclosed embodiments include a content consumption monitor (CCM) determines surges in content consumption based on changes in content consumptions scores. The CCM determines the content consumptions scores for domains and/or organizations (orgs) based on session events generated by different devices/users from the org and/or domain, a number of events generated by the org/domain, content and/or user interactions with the content indicated by the events, relevancy scores of the content to one or more topics, and/or other criteria. The CCM detects surges in consumption or interest in a topic for the domain/org when the consumption score reaches a threshold and/or within a period of time. The CCM may adjust the consumption score based on the changes in the relevancy, number of events and/or the number of users over different time periods. Other embodiments may be described and/or claimed.

Abstract: A content consumption monitor (CCM) generates intent data that identifies topics of real-time interest to users. The CCM uses the intent data to direct information to more interested audiences and reduce the information noise/overload that may prevent users from viewing information. This targeted information may increase user conversion rates for seminars, advertising, documents downloads, or any other activity associated with published information. In one example, the CCM generates the intent data from third party content. Using a wide variety of third party content enables the CCM to better identify current user interests. The CCM may aggregate the intent data for different demographics, such as for companies, job positions, age, gender, or geographic locations.

Abstract: Disclosed embodiments include a content consumption monitor (CCM) determines surges in content consumption based on changes in content consumptions scores. The CCM determines the content consumptions scores for domains and/or organizations (orgs) based on session events generated by different devices/users from the org and/or domain, a number of events generated by the org/domain, content and/or user interactions with the content indicated by the events, relevancy scores of the content to one or more topics, and/or other criteria. The CCM detects surges in consumption or interest in a topic for the domain/org when the consumption score reaches a threshold and/or within a period of time. The CCM may adjust the consumption score based on the changes in the relevancy, number of events and/or the number of users over different time periods. Other embodiments may be described and/or claimed.

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: 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: Disclosed embodiments include a distributed hyperparameter (HP) tuning system, which includes a manager and a plurality of trainers. The manager continuously estimates HP sets for a machine learning (ML) model and distributes each HP set to respective trainers. Each trainer obtains a respective HP set and trains a local version of the ML model using the respective HP set. Each trainer determines a performance value for an HP sets used to train its local version of the ML model, and sends the performance value and the HP set to the manager. The manager estimates a new HP set from the HP set received from each trainer. The HP set estimation continues until convergence takes place. 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 content consumption monitor (CCM) generates intent data that identifies topics of real-time interest to users. The CCM uses the intent data to direct information to more interested audiences and reduce the information noise/overload that may prevent users from viewing information. This targeted information may increase user conversion rates for seminars, advertising, documents downloads, or any other activity associated with published information. In one example, the CCM generates the intent data from third party content. Using a wide variety of third party content enables the CCM to better identify current user interests. The CCM may aggregate the intent data for different demographics, such as for companies, job positions, age, gender, or geographic locations.

Abstract: A content consumption monitor (CCM) generates intent data that identifies topics of real-time interest to users. The CCM uses the intent data to direct information to more interested audiences and reduce the information noise/overload that may prevent users from viewing information. This targeted information may increase user conversion rates for seminars, advertising, documents downloads, or any other activity associated with published information. In one example, the CCM generates the intent data from third party content. Using a wide variety of third party content enables the CCM to better identify current user interests. The CCM may aggregate the intent data for different demographics, such as for companies, job positions, age, gender, or geographic locations.

Abstract: A content consumption monitor (CCM) generates intent data that identifies topics of real-time interest to users. The CCM uses the intent data to direct information to more interested audiences and reduce the information noise/overload that may prevent users from viewing information. This targeted information may increase user conversion rates for seminars, advertising, documents downloads, or any other activity associated with published information. In one example, the CCM generates the intent data from third party content. Using a wide variety of third party content enables the CCM to better identify current user interests. The CCM may aggregate the intent data for different demographics, such as for companies, job positions, age, gender, or geographic locations.

Abstract: A content consumption monitor (CCM) identifies events from a domain, a number of the events, content associated with the events, and a relevancy of the content to a topic. The CCM may generate a consumption score for the domain and topic based on the number of events and the relevancy of the content to the topic. The CCM may identify domain surges for the topic when the consumption score reaches a threshold. The CCM may adjust the consumption score based on the changes in the relevancy, number of events and/or the number of users over different time periods. The consumption scores not only identify companies interested in different topics but also identifies consumption surges that indicate when the companies are most receptive to direct contacts regarding those topics.