A computer-implemented method for generating a search query completion suggestion by a search engine by receiving an indication of at least a portion of a search query from an electronic device; generating, based on the indication, a ranked set of search query completion suggestions; analyzing a top one of the ranked set of search query completion suggestions to determine if the top one of the ranked set of search query completion suggestions meets a pre-determined trigger condition; in response to a positive outcome, generating a set of search results that are responsive to an intermediate search query that includes the at least the portion of the search query and the top one of the ranked set of search query completion suggestions; transmitting to the electronic device: the ranked set of search query completion suggestions; and a Search Engine Result Page (SERP) containing the set of search results.

Methods, apparatuses, and systems for content management and search refinement are described. Embodiments of the present disclosure include search refinement systems configured to identify the most relevant data attributes (e.g., facets) for filtering search results. For example, a user may provide a search query, and a search engine may retrieve a set of search results in addition to providing facets (e.g., data attributes associated with search result objects from the retrieved set of search results) for user search refinement of the retrieved search results. In some embodiments, facets are scored using a significance heuristic, which is used to determine and select facets that provide the most information gain given a set of search results. Selected facets may be presented to a user as filtering options for narrowing a retrieved set of results.

Methods, apparatuses, and systems for content management and search refinement are described. Embodiments of the present disclosure include search refinement systems configured to identify the most relevant data attributes (e.g., facets) for filtering search results. For example, a user may provide a search query, and a search engine may retrieve a set of search results in addition to providing facets (e.g., data attributes associated with search result objects from the retrieved set of search results) for user search refinement of the retrieved search results. In some embodiments, facets are scored using a significance heuristic, which is used to determine and select facets that provide the most information gain given a set of search results. Selected facets may be presented to a user as filtering options for narrowing a retrieved set of results.

The present disclosure provides a method and apparatus for determining an extended reading content, a device, and a storage medium, relating to the field of data processing. The method may include: displaying a target page, in response to a viewing request to the target page; giving a reading prompt to an extended reading resource in the target page, based on a corresponding relationship between a requirement recognition result of at least one target reading content in the target page and the extended reading resource; and displaying the extended reading resource, in response to the viewing request matching the reading prompt.

Aspects of the present disclosure are directed to a computer-implemented method including receiving, by a user computing device, data that describes a destination for the media content item. Example destinations can include a location of a recipient of message including the media content item and a digital location (e.g., website, social networking page, etc.). The method can include selecting, by a computing system comprising the user computing device, one or more media content items based on the data that describes the destination for the media content item. Media content items that are more relevant and/or appropriate can be selected by considering the destination of the media content item. The selected media content item(s) can be provided for display by the user computing device in a dynamic keyboard interface.

An apparatus for use in a communication system sets, in a search request serving to retrieve, from a storage entity, records that match filters, an indication which indicates if, for one or more matching records of the records that match the filters, a content of the one or more matching records is to be received in a response to the search request along with references to the records that match the filters, and transmits the search request including the indication.

A method includes receiving, by a processing device, a command search query for searching a command search history, identifying, by the processing device, a command from the command search history in view of the command search query, determining, by the processing device, that the command comprises a replaceable parameter, identifying, by the processing device, a content source corresponding to the replaceable parameter, and generating, by the processing device, an updated command by replacing the replaceable parameter with a data item from the content source.

In an example embodiment, a technique is presented that accesses training data that includes information about items, queries for items, and labels for the combinations of items and queries. The labels may correspond to different events, and there may be multiple different labels for the same combination of item and query. A machine learned model is then trained to learn a function for embedding each item to which a label pertains and a function for embedding each query to which a label pertains. Then, for each item in the training data, the items are embedded using the machine learned model, and the item embeddings for the item are concatenated into a single item embedding. At inference time, a similar concatenation is performed for multiple query embeddings. The concatenated embeddings are then used as input to an approximate k-nearest neighbor search function.

In an example embodiment, a technique is presented that accesses training data that includes information about items, queries for items, and labels for the combinations of items and queries. The labels may correspond to different events, and there may be multiple different labels for the same combination of item and query. A machine learned model is then trained to learn a function for embedding each item to which a label pertains and a function for embedding each query to which a label pertains. Then, for each item in the training data, the items are embedded using the machine learned model, and the item embeddings for the item are concatenated into a single item embedding. At inference time, a similar concatenation is performed for multiple query embeddings. The concatenated embeddings are then used as input to an approximate k-nearest neighbor search function.

A method and system for analyzing customer experience from unstructured social media data comprising, fetching data from the social media platforms and segregate these social media conversations into campaign data, a “True social” data, and news data using the industry topic related keywords. Furthermore, it also identifies from the true social data, different stages of customer experience such as a pre-experience data, a during-experience data and a post-experience data. It enables identification of at risk customers, loyal customers, and one or more target customer of a brand. Customer issue areas may also be identified that a brand needs to focus on along with identification of key social media influencers who are influencing conversations for or against brand and enables the brand to take appropriate action based on the analysis of various posts.