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: 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 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 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: 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.

Abstract: A distributed model generation system includes a master node that estimates parameter sets for a topic classification (TC) model. The estimated parameter sets are loaded into a queue. Multiple training nodes download the estimated parameter sets from the queue for training associated TC models. The training nodes generate model performance values for the trained TC models and send the model performance values back to the master node. The master node uses the model performance values and the associated parameter sets to estimate additional TC model parameter sets. The master node estimates new parameter sets until a desired model performance value is obtained. The master node may use a Bayesian optimization to more efficiently estimate the parameter sets and may distribute the high processing demands of model training and testing operations to the training nodes.

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.