There is provided a method, comprising: extracting user feature profiles for users of a social network, each feature profile being structured and including user features extracted from unstructured user generated text, indications of participation in groups, and structured user profiles, training a clustering-component of a model to cluster the feature profiles, training a matching-component of the model to compute a distance score indicative of statistical similarity between a feature profile of a target user and features profiles of other users of a same cluster, using a training dataset of pairs of feature profiles extracted from common clusters, each pair assigned a distance score label, providing the model for: identifying a certain cluster of a certain user, and computing distance scores between the feature profile of the certain user and other feature profiles of other users of the certain cluster for selecting one user for establishment of a communication session.

Described is an open communication system. The system includes a server having a memory storing user data and a first user computing device coupled to the server. The server may be programmed to allow multiple user computing devices to connect to the server and the server determines if the user computing devices are within a predetermined proximity to each other and whether the same communication channel is selected. All of the user computing devices that have selected the same communication channel and are within the predetermined proximity to each may be connected in an open communication link that allows the connected user to communicate. The system may include the option of establishing and invite particular users to a private or less used channel.

A computer-implemented, network-connected content recommender generating content recommendations for a plurality of content servers hosted by one or more customers, the content recommender comprising: one or more processors; a memory storing instruction that, when executed by the one or more processors, cause the recommender to perform operations comprising: receiving a plurality of content recommendation requests from a querying one of said customer content servers via a plurality of input streams, each input stream including a data repository; outputting data, from the memory, associated with the content recommendation requests; receiving some or all of the data associated with said content recommendation requests; generating a first model-specific recommendation result from a first set of the plurality of received data; generating a second model-specific recommendation result from a second set of the plurality of received data; combining the first model-specific recommendation results with the second model-specific results to generate an ensemble recommendation result; and transmitting the ensemble result from the content recommender to said querying customer content server.

An approach is described for addressing propagation of inaccurate information in a social networking environment. An associated method may include identifying inaccurate information within the social networking environment, facilitating creation of countering content to address the inaccurate information, and disseminating the countering content. The countering content may be determined by identifying behavior of one or more users among a plurality of users within the social networking environment. Identifying the inaccurate information within the social networking environment may include receiving information provided within the social networking environment. Upon determining that the received information is factual and thus objectively verifiable, it may be determined whether the received information matches analogous information verified as accurate. Upon determining that the received information does not match the analogous information verified as accurate, the received information may be marked as inaccurate.

Stepwise relationship cadence management can include generating a discourse cadence and confidence (DCC) measure based on a response message. The response message is made in replying to an originating message during a multi-party discourse over an electronic communication channel. The DCC measure indicates a likelihood of improving cadence and confidence with respect to an originator of the originating message and is based on a stepwise relational confidence model (SRCM) generated from an analysis of a plurality of prior multi-party discourses. Stepwise relationship cadence management can also include prompting a user to provide a follow-on message in response to determining that the response message made in replying to the originating message is not likely to improve cadence and confidence.

A method for calculating a total transmission probability within a social network based on timing includes a path probability calculating step, a first binary-addition tree searching step, a second binary-addition tree searching step and a transmission probability calculating step. The path probability calculating step is performed to calculate a plurality of time-path probability matrices from the social network. The first binary-addition tree searching step is performed to enumerate a plurality of feasible spread vectors and a plurality of 1-lag temporal vectors. The second binary-addition tree searching step is performed to enumerate a plurality of time-slot vectors of each of the 1-lag temporal vectors. The transmission probability calculating step is performed to calculate the total transmission probability of the social network. The time-path probability matrices are corresponding to a plurality of time values, and the time values are in the specific time and different from each other.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

A messaging system provides recommendations of content that account holders of the messaging system might be interested in engaging with. In order to determine what to recommend, the messaging system generates a model of account holder engagement behavior organized by type of engagement. The model parameters are trained on differences between expected engagement behavior based on past data and actual engagement behavior, and include a set of common factor matrices that are trained using data from more than on engagement type. As a consequence, engagement behavior of other account holders with respect to other types of engagements different than the one sought to be recommended serves as a partial basis for determining what engagements of the sought-after type are recommended.

Systems and methods for screenless computerized social-media access may include (1) producing, via an audio speaker that is communicatively coupled to a computing device, a computer-generated verbal description of a social-media post provided via a social-media application, (2) detecting, via a microphone that is communicatively coupled to the computing device, an audible response to the social-media post from a user of the computing device, and (3) digitally responding to the social-media post in accordance with the detected audible response. Various other methods, systems, and computer-readable media are also disclosed.

A computer implemented system for an intelligent timeline includes computer readable instructions to operate a timeline engine, a logic extraction and application engine, a calendar engine, a performance evaluation engine, an advertisement placement engine, and a social networking engine that are interconnected to one another. The timeline engine creates a timeline of events containing external events and/or an owner's actions. Each event has a timestamp such that the events may be arranged in the order of timestamps. The logic extraction and application engine extracts the logical inferences from the events to be used by the timeline engine. The calendar engine creates a calendar containing the events and other reminders. The performance evaluation engine creates performance evaluation results of an owner's actions based on the events. The timeline of an owner may be sold or shared on the owner's social networking channel to subscribers. Advertisement placement engine facilitates advertisement transactions related to the timelines.