Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.

A method for improving communications in a digital collaboration environment by receiving a communication directed to a first user, determining that the first user is unavailable, in response to determining that the first user is unavailable, determining a second user based on an attribute, and notifying the second user about the communication directed to the first user.

A method for improving communications in a digital collaboration environment by receiving a communication directed to a first user, determining that the first user is unavailable, in response to determining that the first user is unavailable, determining a second user based on an attribute, and notifying the second user about the communication directed to the first user.

Provided are a method for pushing anchor information, a computer device, and a storage medium. The method for pushing the anchor information includes: recalling an anchor user; determining an anchor type of the anchor user by using a live streaming history and a live streaming efficiency as classification dimensions; calculating an interaction score of the anchor user, wherein the interaction score represents a feature of a viewer user viewing a live streaming room of the anchor user; calculating a comprehensive score of the anchor user based on the interaction score and the anchor type; and pushing anchor information of the anchor user to the viewer user based on the comprehensive score.

In accordance with an embodiment, described herein are systems and methods for auto-completion of ICS flow using artificial intelligence/machine learning. Next actions prediction is a service that assists users in modeling the flows quickly by predicting and suggesting the next set of actions a user might be thinking of adding. The service also assists the user to follow some of the best practices while creating an integration flow.

A method is provided of communication between a user and a database of Patents and also of the display and the interactive exploration of data on information of interest relating to Patents/Patent applications. The method comprises: the generation, by means of an access interface, of a request allowing the database to be interrogated based on at least one selection criterion entered into the access interface; the interrogation of the database by means of the request and the loading of bibliographical data for the Patents/Patent applications found, the downloaded bibliographical data comprising data on the technological category; the processing of the bibliographical data, the processing comprising an analysis of co-occurrences comprising the determination of a number of co-occurrences of data on the technological category for all of the Patents/Patent applications found; the displaying, in interactive graphical and/or textual form, of a result and/or of an interpretation of the analysis of co-occurrences.

A method is provided of communication between a user and a database of Patents and also of the display and the interactive exploration of data on information of interest relating to Patents/Patent applications. The method comprises: the generation, by means of an access interface, of a request allowing the database to be interrogated based on at least one selection criterion entered into the access interface; the interrogation of the database by means of the request and the loading of bibliographical data for the Patents/Patent applications found, the downloaded bibliographical data comprising data on the technological category; the processing of the bibliographical data, the processing comprising an analysis of co-occurrences comprising the determination of a number of co-occurrences of data on the technological category for all of the Patents/Patent applications found; the displaying, in interactive graphical and/or textual form, of a result and/or of an interpretation of the analysis of co-occurrences.

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.

Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. A server retrieves a set of nodes and generates a primary prediction model using data aggregated based on all nodes. The server then executes various clustering algorithms in order to segment the nodes into different clusters. The server then generates a secondary (corrective) prediction model to calculate a correction needed to improve the results achieved by executing the primary prediction model for each cluster. When a node with unknown/limited data and attributes is identified, the server identifies a cluster most similar the new node and further identifies a corresponding secondary prediction model. The server then executes the primary prediction model in conjunction with the identified secondary prediction model to populate a graphical user interface with an accurate predicted future attribute for the new node.

Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. A server retrieves a set of nodes and generates a primary prediction model using data aggregated based on all nodes. The server then executes various clustering algorithms in order to segment the nodes into different clusters. The server then generates a secondary (corrective) prediction model to calculate a correction needed to improve the results achieved by executing the primary prediction model for each cluster. When a node with unknown/limited data and attributes is identified, the server identifies a cluster most similar the new node and further identifies a corresponding secondary prediction model. The server then executes the primary prediction model in conjunction with the identified secondary prediction model to populate a graphical user interface with an accurate predicted future attribute for the new node.