Abstract: The present disclosure is intended to provide a smart television and a method for displaying a graphical user interface of a television screen shot. The method includes while a display device is displaying currently-played content, in response to receiving an input instruction for capturing a screen shot, acquiring a screen shot image comprising at least one object; and while the display device continues playing, displaying a screen shot content display layer on the display device. The screen shot content display layer is configured to present the screen shot image. The method further includes in response to receiving an input for selecting an object or a keyword matched with the object, displaying recommended content related to the object; in response to receiving a selection for a different object on the screen shot image by moving a focus frame, updating presentation of recommended content based on the selected different object.

Abstract: A solid pharmaceutical composition containing a 1,3,5-triazine derivative or a pharmaceutically acceptable salt thereof and a preparation method therefor. In particular, involved are a solid pharmaceutical composition containing 4-(tert-butoxyamino)-6-(6-(trifluoromethyl)pyridin-2-yl)-N-(2-(trifluoromethyl)pyridin-4-yl)-1,3,5-triazine-2-amine or a pharmaceutically acceptable salt thereof and a preparation method therefor. The solid pharmaceutical composition has a good stability, a fast dissolution rate and a high bioavailability, and is suitable for clinical production and use.

Abstract: The present disclosure is intended to provide a smart television and a method for displaying a graphical user interface of a television screen shot. The method includes while a display device is displaying currently-played content, in response to receiving an input instruction for capturing a screen shot, acquiring a screen shot image comprising at least one object; and while the display device continues playing, displaying a screen shot content display layer on the display device. The screen shot content display layer is configured to present the screen shot image. The method further includes in response to receiving an input for selecting an object or a keyword matched with the object, displaying recommended content related to the object; in response to receiving a selection for a different object on the screen shot image by moving a focus frame, updating presentation of recommended content based on the selected different object.

Abstract: In an example, an indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. Then a plurality of user profiles in the social networking service are accessed. A machine-learned model is then used to calculate, based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred, a similarity score between a first node and second node by computing distance between the first node and the second node in a d-dimensional space on which a plurality of entities are mapped, the similarity score generated using a generalized linear mixed model having a global coefficient vector applied to global function pertaining to the co-occurrence counts and a first random effects coefficient vector applied to a random effects per-country function.

Abstract: In an example embodiment, two machine learned models are trained. One is trained to output a probability that a searcher having a member profile in a social networking service will select a potential search result. The other is trained to output a probability that a member corresponding to a potential search result will respond to a communication from a searcher. Features may be extracted from a query, information about the searcher, and information about the member corresponding to the potential search result and fed to the machine learned models. The outputs of the machine learned models can be combined and used to rank search results for returning to the searcher.

Abstract: The present invention relates to the technical field of televisions, and provides a method for processing a television screenshot, a smart television, and a storage medium. To meet the demands of a more intuitive user interface and a seamless user interaction function, multiple sets of optional bars are displayed while displaying current playback content on a display screen in response to an input screenshot operation instruction, wherein optional bars are respectively used for displaying a picture thumbnail of a screenshot, recognizing content-related recommended content on the basis of an image of the screenshot, and/or responding to a user control instruction input interface for an operation associated with the screenshot.

Abstract: The present disclosure is intended to provide a smart television and a method for displaying a graphical user interface of a television screen shot. The method includes while a display device is displaying currently-played content, in response to receiving an input instruction for capturing a screen shot, acquiring a screen shot image comprising at least one object; and while the display device continues playing, displaying a screen shot content display layer on the display device. The screen shot content display layer is configured to present the screen shot image. The method further includes in response to receiving an input for selecting an object or a keyword matched with the object, displaying recommended content related to the object; in response to receiving a selection for a different object on the screen shot image by moving a focus frame, updating presentation of recommended content based on the selected different object.

Abstract: In an example, a plurality of user profiles in a social networking service are accessed. A heterogeneous graph structure having a plurality of nodes connected by edges is generated, each node corresponding to a different entity in the social networking service, each edge representing a co-occurrence of entities represented by nodes on each side of the edge in at least one of the user profiles. Weights are calculated for each edge of the heterogeneous graph structure, the weights being based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred. The heterogeneous graph structure is embedded into a d-dimensional space. A machine-learned model is then used to calculate a similarity score between a first node and second node by computing distance between the first node and the second node in the d¬-dimensional space.

Abstract: In an example, a deep learning network is used to calculate a similarity score between a first query in a social networking service and each of one or more suggestable entities in the social networking service. The suggestable entities are determined via a first machine learned model. The deep learning network takes as input the suggestable entities as well as a history of interactions with a graphical user interface of a social networking service by a first member of the social networking service, a history of queries performed via the graphical user interface by the first member, and the first query itself.

Abstract: One aspect of this disclosure relates to presenting a user with a stimulus to elicit user interaction with a task on a computing platform associated with the user. The stimulus may be presented on the computing platform when a set of triggering criteria is satisfied. The stimulus includes a task for the user to complete. The stimulus prompts the user to complete the task. The task includes a set of task criteria for completion. Responsive to the user satisfying the set of task criteria, the user is presented with one or more options to modify the stimulus. The user may be continuously prompted by the stimulus until the set of task criteria is satisfied.

Abstract: A system and method includes receiving a search query and obtaining, from a database, member data of a member. For each of a plurality of nonlinear models, the nonlinear model is traversed based on a comparison of characteristics to conditions to obtain a score, wherein, among the nonlinear models, at least one characteristic is an inferred characteristic based on at least one of: activities by the member in an online networking system; and connections by the member in the online networking system. The score obtained from each of the nonlinear models is combined to obtain a combined score and a user interface to displays information related to the member based, at least in part on the combined score.

Abstract: One aspect of this disclosure relates to presenting a user with a stimulus to elicit user interaction with a task on a computing platform associated with the user. The stimulus may be presented on the computing platform when a set of triggering criteria is satisfied. The stimulus includes a task for the user to complete. The stimulus prompts the user to complete the task. The task includes a set of task criteria for completion. Responsive to the user satisfying the set of task criteria, the user is presented with one or more options to modify the stimulus. The user may be continuously prompted by the stimulus until the set of task criteria is satisfied.

Abstract: System and techniques for search result refinement are described herein. Search results and a search context may be obtained. A context dependent facet set may be added to a search result in the search results. A user interface of the context dependent facet set may be presented in conjunction with displaying the search results. A selection of a facet in the context dependent facet set may be received from a user. The search results being displayed may be filtered such that search results that meet a measurement of the facet are included in the displayed search results and the remaining search results are excluded from the display.

Abstract: System and techniques for facet expansion are described herein. A user interface element may be presented on facet selection portion of a search result display including search results. Here, the user interface element is arranged to accept user input of a facet. Partial user input for a facet may be received. A peer entity to an entity corresponding to the facet may be obtained. A peer facet may be presented in a suggestion element in the facet selection portion in response to receiving the partial user input.

Abstract: In an example, a deep learning network is used to calculate a similarity score between a first query in a social networking service and each of one or more suggestable entities in the social networking service. The suggestable entities are determined via a first machine learned model. The deep learning network takes as input the suggestable entities as well as a history of interactions with a graphical user interface of a social networking service by a first member of the social networking service, a history of queries performed via the graphical user interface by the first member, and the first query itself.

Abstract: An indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. A plurality of user profiles in the social networking service is accessed. A first machine-learned model is used to learn embeddings for the plurality of different entities in a d-dimensional space. A second machine-learned model is used to learn an embedding for each of one or more query terms that are not contained in the indication of the plurality of different entities in the social networking service, using the embeddings for the plurality of different entities learned using the first machine-learned model, the second-machine learned model being a deep structured semantic model (DSSM). A similarity score between a query term and an entity is calculated by computing distance between the embedding for the query term and the embedding for the entity in the d-dimensional space.

Abstract: In an example, a plurality of user profiles in a social networking service are accessed. A heterogeneous graph structure comprising a plurality of nodes connected by edges is generated, each node corresponding to a different entity in the social networking service, each edge representing a co-occurrence of entities represented by nodes on each side of the edge in at least one of the user profiles. Weights are calculated for each edge of the heterogeneous graph structure, the weights being based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred. The heterogeneous graph structure is embedded into a d-dimensional space. A machine-learned model is then used to calculate a similarity score between a first node and second node by computing distance between the first node and the second node in the d-dimensional space.

Abstract: Techniques for implementing a deep neural network architecture for search are disclosed herein. In some embodiments, the deep neural network architecture comprises: an item neural network configured to, for each one of a plurality of items, generate an item vector representation based on item data of the one of the plurality of items; a query neural network configured to generate a query vector representation for a query based on the query, the query neural network being distinct from the item neural network; and a scoring neural network configured to, for each one of the plurality of items, generate a corresponding score for a pairing of the one of the plurality of items and the query based on the item vector representation of the one of the plurality of items and the query vector representation, the scoring neural network being distinct from the item neural network and the query neural network.

Abstract: A system and method includes receiving a search query and obtaining, from a database, member data of a member. For each of a plurality of nonlinear models, the nonlinear model is traversed based on a comparison of characteristics to conditions to obtain a score, wherein, among the nonlinear models, at least one characteristic is an inferred characteristic based on at least one of: activities by the member in an online networking system; and connections by the member in the online networking system. The score obtained from each of the nonlinear models is combined to obtain a combined score and a user interface to displays information related to the member based, at least in part on the combined score.

Abstract: In an example embodiment, two machine learned models are trained. One is trained to output a probability that a searcher having a member profile in a social networking service will select a potential search result. The other is trained to output a probability that a member corresponding to a potential search result will respond to a communication from a searcher. Features may be extracted from a query, information about the searcher, and information about the member corresponding to the potential search result and fed to the machine learned models. The outputs of the machine learned models can be combined and used to rank search results for returning to the searcher.