Abstract: A processing system may track accessing of a plurality of uniform resource locators among users of a communication network, determine an occurrence of an event of an event type based upon an increased accessing of the plurality of uniform resource locators for at least one time period, identify a first sub-group of the users associated with the event, apply a first action in the communication network to a test group including at least a first portion of the first sub-group, the first action addressing a demand associated with the event of the event type, track at least a first success rate of the first action for the first portion of the first sub-group, and apply the first action to at least a second portion of the first sub-group in response to determining that the first success rate for the first portion of the first sub-group exceeds a threshold success rate.

Abstract: A processing system may identify a plurality of user interaction data associated with a target category of a plurality of users, identify a relevant subset of user interaction data, compress the plurality of user interaction data to the relevant subset of user interaction data, train a machine learning model with the relevant subset of user interaction data, obtain additional user interaction data associated with an additional user, identify a relevant subset of the additional user interaction data, apply the relevant subset of the additional user interaction data as an input to the machine learning model, obtain an output of the machine learning model quantifying a measure of which the relevant subset of the additional user interaction data is indicative of the target category, and perform at least one action responsive to the measure of which the relevant subset of the additional user interaction data is indicative of the target category.

Abstract: A processing system may identify a plurality of uniform resource locators associated with a target category of a plurality users of a communication network, identify a plurality of sequences of URLs, each sequence comprising URLs from among the plurality of URLs, each sequence associated with a user known to be of the target category, and train a machine learning model with the plurality of sequences to detect additional sequences that are indicative of the target category. The processing system may next obtain a set of URLs associated with an additional user, identify a sequence comprising URLs, from among the plurality of URLs, that are contained within the set of URLs, apply the sequence as an input to the machine learning model that has been trained, and obtain an output of the machine learning model quantifying a measure of which the sequence is indicative of the target category.