Systems and methods are provided herein for displaying recommendations that are temporally relevant to activities of a user and are contextually relevant to a portion of viewed media. This may be accomplished by a media guidance application identifying activities a user has planned, as well as respective times for which each of the plurality of activities was planned to be performed by the user. The media guidance application may filter the activities by determining which of the first plurality of activities has a corresponding respective time that is within a threshold period of time. The media guidance application may then identify characteristics of each filtered activity and of a media asset that the user is consuming at the present moment, and may compute whether the characteristics match. If the characteristics match, the media guidance application may generate for display a recommendation relating to the respective activity.

A server obtains user data for a respective user, including data corresponding to the respective user's consumption of media in a first content domain. Before obtaining, for the respective user, data corresponding to a second content domain, the server uses a neural network to generate a user embedding for the respective user based on the user data. The server generates, for a plurality of content items of the second content domain consumed by users other than the respective user, a respective content item embedding. The respective content item embedding is based on user embeddings of the at least one user other than the respective user. The server system determines, using the user embedding for the respective user and respective content item embeddings, a first content item in the second content domain that meets matching criteria for the respective user and provides the first content item to the respective user.

Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for integrating user-specific context indicators into a searchable enterprise platform. In one embodiment, an apparatus is configured to apply a learned user profile, to a set of search results to obtain a user-specific, ranked arrangement of search results. The learned user profile may be developed through the application of a machine learning and/or trained model to a set of user behaviors that have been determined or otherwise detected within an enterprise platform, such that the user-specific context in which a user's search arises can be modeled and applied to retrieved digital content items associated with a search query within the enterprise platform.

Methods, systems, and apparatus for receiving a request that includes a user identifier of a user that submitted a search query and an entity identifier of an entity that is referenced by the search query, identifying a plurality of knowledge elements that are related to the entity, identifying, in a consumption database, one or more items that have been indicated as consumed by the user and that are associated with the entity that is referenced by the search query, assigning rank scores to the plurality of knowledge elements, based at least on identifying the one or more items, selecting one or more of the knowledge elements from among the knowledge elements based at least on the rank scores assigned to the knowledge elements, and providing, in response to the request, information associated with the entity and the one or more selected knowledge elements.

A method for execution by a computing entity for creating a learning tool regarding a topic includes issuing a representation of a first set of physicality assessment assets of a first learning object to a second computing entity. The method further includes obtaining a first assessment response in response to the representation of the first set of physicality assessment assets. The method further includes selecting one of the first learning object and a second learning object as an active learning object based on the first assessment response.

The present invention provides a novel app called “Gayle Mail” that aggregates email inboxes and filters them using tags for items, subjects, and senders to ensure important emails are never missed. The app allows users to prioritize their email accounts, avoiding inbox clutter. Users can manually enter pre-filters or utilize the app's artificial intelligence capabilities to automatically configure settings based on user preferences and historical usage patterns.

In some examples, a computing device may initiate audible play back of a book in response to an instruction from a user. While the book is being audibly played back, the computing device may determine (e.g., in the background) that a content of a recently played back portion of the book matches one or more interests associated with a user, initiate a search, and receive search results. The computing device may re-rank the search results based on the user's interests, pause play back of the book, and audibly read out the re-ranked search results. If the user asks a question, the computing device may pause play back of the book, perform an additional search, and read out loud the additional search results. The computing device may resume the audible play back of the book after the user provides a resume play back instruction.

Disclosed in some examples are methods, systems, and machine-readable mediums which provide for an agent support application with a plurality of plug-in communication support assistants. Each of the plurality of plug-in communication support assistants monitors communications between the agents and customers for different conversational triggers. Conversational triggers may be any conversation, either by the agent or the customer, that the communication support assistant is trained to detect. Upon detecting one of these conversational triggers, the plug-in communication support assistant provides one or more suggestions to the agent.

A method for execution by a computing entity for creating a learning tool regarding a topic includes issuing a representation of a first set of physicality assessment assets of a first learning object to a second computing entity. The method further includes obtaining a first assessment response in response to the representation of the first set of physicality assessment assets. The method further includes selecting one of the first learning object, a second learning object, and a third learning object as an active learning object based on the first assessment response.

Media preferences for a user account (or for multiple user accounts) may be used to guide media playback. A server of a streaming audio service receives, from a first controller interface on a first mobile device, data representing a request to play back a first audio track on a playback device, the first controller interface associated with a first user account of the streaming audio service. In response, the server streams data representing the first audio track to the playback device for playback. While streaming, the server receives, from a second controller interface on a second mobile device, data representing a preference for the first audio track that the playback device is playing back, the second controller interface associated with a second user account of the streaming audio service. The server stores, in a preference database corresponding to the second user account, data representing the preference for the first audio track.