Provided is a method of managing a landing page by an electronic apparatus, in which the method includes acquiring first information on a user terminal and second information for the landing page in response to a request for accessing the landing page from the user terminal, generating address information for the landing page based on the first information and the second information, and providing the landing page to the user terminal based on the address information.

A method, a computer program product, and a computer system determine, based on reactions exhibited by a user while consuming information, options for further information. The method includes determining a user reaction while the user is consuming the information. The user reaction is indicative of the user implicitly requesting the further information. The information is presented based on the user selecting from original search results according to an original search string submitted by the user. The method includes determining a portion of the information corresponding to the user reaction being exhibited. The method includes determining a further search string corresponding to the portion of the information. The method includes receiving further search results based on the further search string. Each of the further search results configured to present the further information to the user. The method includes modifying the original search results to include the further search results.

The present disclosure provides an error correction method. The error correction method includes: determining a plurality of target candidate entities from a preset dictionary tree based on a query request; for each target candidate entity, calculating a first probability that the target candidate entity is a legitimate entity; evaluating each target candidate entity to obtain an evaluation result, a target candidate entity corresponding to an evaluation result; and determining a real intent entity corresponding to the query request based on the first probability and the evaluation result.

A framework for generating and presenting verbal command suggestions to facilitate discoverability of commands capable of being understood and support users exploring available commands. A target associated with a direct-manipulation input is received from a user via a multimodal user interface. A set of operations relevant to the target is selected and verbal command suggestions relevant to the selected set of operations and the determined target are generated. At least a portion of the generated verbal command suggestions is provided for presentation in association with the multimodal user interface in one of three interface variants: one that presents command suggestions as a list, one that presents command suggestions using contextual overlay windows, and one that presents command suggestions embedded within the interface. Each of the proposed interface variants facilitates user awareness of verbal commands that are capable of being executed and teaches users how available verbal commands can be invoked.

A system, method, and computer-readable medium for proving feedback on database instructions, identifying, for example, existing patterns and providing suggested replacement instructions. This may have the effect of improving the efficiency of instructions used to create and/or manipulate databases. According to some aspects, these and other benefits may be achieved by parsing received instructions into an organizational structure, traversing the organizational structure for known patterns, and suggesting replacement patterns. In implementation, this may be effected by receiving one or more sets of known patterns and corresponding replacement patterns, parsing received instructions, comparing the known patterns with the parsed instructions, and providing suggested replacement patterns based on one or more known patterns matching the parsed instructions. A benefit of may include reducing Cartesian products during the merging of tables.

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.

Various embodiments provide for associating a collection of media items with a graphical element. For instance, a system can: generate corpus data from a set of features of a collection of media content items; determine a set of candidate graphical elements for the collection of media content items based on the corpus data and further based on a set of first mappings associating at least one graphical element and at least one n-gram; determine a set of prediction scores corresponding to the set of candidate graphical elements based on the set of features; determine a ranking for the set of candidate graphical elements based on the set of prediction stores; select a set of predicted graphical elements, from the set of candidate graphical elements, based on the ranking; and provide the set of predicted graphical elements in association with the collection of media content items.

Methods, systems and programming for providing query suggestions based on user feedback. In one example, a prefix of a query is first received. An input including a prefix of a query is received from a user in a search session. A plurality of query suggestions are fetched based on the prefix of the query. Rankings of the plurality of query suggestions are determined based, at least in part, on the user's previous interactions in the search session with respect to at least one of the plurality of query suggestions. The at least one of the plurality of query suggestions has been previously provided to the user in the search session. The plurality of query suggestions are provided in the search session based on their rankings as a response to the input.

Systems and methods are described herein for generating a search query using flexible autocomplete suggestions. A text input is received, and a plurality of portions of the text input, each corresponding to a different search parameter, are identified. For each of the identified portions, at least one suggested alternate text is retrieved based on the corresponding attribute, and a user interface element is generated for display in which the original text of the respective portion is displayed, along with the suggested alternate texts for that portion, which are selectable by the user. Upon receiving selection of a suggested alternate text in at least one user interface element, a search query is generated based on each portion for which no alternate has been selected, and each selected alternate text. Search request are retrieved in response to the search query, and the results are generated for display to the user.

Embodiments described herein provide a query autocompletion (QAC) framework at subword level. Specifically, the QAC framework employs a subword encoder that encodes or converts the sequence of input alphabet letters into a sequence of output subwords. The generated subword candidate sequences from the subword encoder is then for the n-gram language model to perform beam search on. For example, as user queries for search engines are in general short, e.g., ranging from 10 to 30 characters. The n-gram language model at subword level may be used for modeling such short contexts and outperforms the traditional language model in both completion accuracy and runtime speed. Furthermore, key computations are performed prior to the runtime to prepare segmentation candidates in support of the subword encoder to generate subword candidate sequences, thus eliminating significant computational overhead.