The present disclosure is descriptive of discovering structure, content, and context of a media event, e.g., a live media event, using real-time discussions that unfold through short messaging services. Generally, a sampling of short messages of a plurality of users is obtained. The sampling of short messages corresponds to a media event. A segment in the media event is identified using the sampling of short messages, and at least one term taken from the sampling of short messages is identified. The at least one term is indicative of a context of the identified segment.

A method is provided for creating a lexical tree from a statement in a natural language. The method is implemented by a natural-language processing module. The method includes: receiving a statement in natural language in the form of a string of characters; iteratively processing the statement as a function of at least one processing parameter and one ontological dictionary, delivering at least one relational graph corresponding to at least one lexical item included in the statement in natural language; and creating a data structure at output having all possible combinations of the lexical items of the statement in natural language on the basis of the at least one relational graph.

An example method involves (i) accessing first data representing text, wherein the text defines at least one position representing a particular type of grammatical break between two portions of the text; (ii) identifying, from among the at least one position, a position that is closest to a target position within the text; (iii) based on the identified position within the text, generating second data that represents a proper subset of the text, wherein the proper subset extends from an initial position within the text to the identified position within the text; and (iv) providing output based on the generated second data.

A system for computer-aided escalation can include and/or interface with any or all of: a set of user interfaces (equivalently referred to herein as dashboards and/or hubs), a computing system, and a set of models. A method for computer-aided escalation includes any or all of: receiving a set of inputs; and processing the set of inputs to determine a set of outputs; triggering an action based on the set of outputs; and/or any other processes.

A system for computer-aided escalation can include and/or interface with any or all of: a set of user interfaces (equivalently referred to herein as dashboards and/or hubs), a computing system, and a set of models. A method for computer-aided escalation includes any or all of: receiving a set of inputs; and processing the set of inputs to determine a set of outputs; triggering an action based on the set of outputs; and/or any other processes.

An electronic apparatus includes a processor. The processor is configured to: obtain information about weighted values respectively assigned to a plurality of items having values that represent content features of a plurality of pieces of content, wherein the weighted values are assigned as higher weight values to items corresponding to the content features preferred by according to genres of content; identify a user content viewed by a user of the electronic apparatus; calculate similarities in values of the plurality of items, respectively, between a plurality of pieces of recommendable content and the user content, respectively; calculate recommendation scores of the plurality of pieces of content, based on the calculated similarities and the weighted values assigned to the items according to the genres; and perform a recommendation operation for one or more content pieces, of which the calculated recommendation score is equal to or higher than a preset ranking.

An electronic apparatus includes a processor. The processor is configured to: obtain information about weighted values respectively assigned to a plurality of items having values that represent content features of a plurality of pieces of content, wherein the weighted values are assigned as higher weight values to items corresponding to the content features preferred by according to genres of content; identify a user content viewed by a user of the electronic apparatus; calculate similarities in values of the plurality of items, respectively, between a plurality of pieces of recommendable content and the user content, respectively; calculate recommendation scores of the plurality of pieces of content, based on the calculated similarities and the weighted values assigned to the items according to the genres; and perform a recommendation operation for one or more content pieces, of which the calculated recommendation score is equal to or higher than a preset ranking.

The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.

The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.

Disclosed herein are systems and methods for correlating item data. A system for correlating item data may comprise a memory storing instructions and at least one processor configured to execute instructions to perform operations comprising: receiving reference text data associated with a reference item from a device; receiving reference image data associated with the reference item from the remote device; determining candidate text data and candidate image data associated with at least one candidate item; selecting a text correlation model; determining a first similarity score by applying the text correlation model to the reference text data and the candidate text data; selecting an image correlation model; determining a second similarity score by applying the image correlation model to the reference image data and the candidate image data; calculating a confidence score based on the first and second similarity scores; and performing a responsive action based on the calculated confidence score.