A method for rendering context based information on a user interface includes receiving a user request to extract the context based information from a database. The database includes a plurality of documents and the request includes at least one search criteria required to determine a context of the user request. The method includes generating a list of documents corresponding to the context of the user request and rendering on a viewing portion of the user interface the list of documents corresponding to the context of the user request.

A method for rendering context based information on a user interface includes receiving a user request to extract the context based information from a database. The database includes a plurality of documents and the request includes at least one search criteria required to determine a context of the user request. The method includes generating a list of documents corresponding to the context of the user request and rendering on a viewing portion of the user interface the list of documents corresponding to the context of the user request.

A visual search recommendation engine utilizes an image quality indication model for a visual search recommendation. Specifically, the visual search recommendation engine receives a search image as a search query at a search engine. The visual search recommendation engine provides the search image as an input into the image quality indication model, which is trained to output an image quality indication based on image aspects of the search image. A plurality of images are identified from an image corpus. The visual search recommendation engine determines an image similarity based on a comparison between the plurality of images from the image corpus and the search image. The image quality indication and the image similarity indicate a search query performance for the search image. A first image exceeding the search query performance is identified. The first image is provided for display at the search engine.

A visual search recommendation engine utilizes an image quality indication model for a visual search recommendation. Specifically, the visual search recommendation engine receives a search image as a search query at a search engine. The visual search recommendation engine provides the search image as an input into the image quality indication model, which is trained to output an image quality indication based on image aspects of the search image. A plurality of images are identified from an image corpus. The visual search recommendation engine determines an image similarity based on a comparison between the plurality of images from the image corpus and the search image. The image quality indication and the image similarity indicate a search query performance for the search image. A first image exceeding the search query performance is identified. The first image is provided for display at the search engine.

A set of queries from an application executing on a client computing device is obtained. A first database based on the set of queries is searched to select a set of event types. A set of predicted parameters associated with the set of event types is sent to the application. The application includes instructions to obtain a first parameter and the set of predicted parameters via a user interface of the application and generate a message comprising the first parameter and an indicator identifying the set of predicted parameters. The first parameter and the indicator are obtained via the second message. A combined query including the first parameter and the set of predicted parameters is generated in response to obtaining the indicator. A vehicle record from the vehicle database is obtained based on the combined query. Values of the vehicle record are sent to the client computing device.

A set of queries from an application executing on a client computing device is obtained. A first database based on the set of queries is searched to select a set of event types. A set of predicted parameters associated with the set of event types is sent to the application. The application includes instructions to obtain a first parameter and the set of predicted parameters via a user interface of the application and generate a message comprising the first parameter and an indicator identifying the set of predicted parameters. The first parameter and the indicator are obtained via the second message. A combined query including the first parameter and the set of predicted parameters is generated in response to obtaining the indicator. A vehicle record from the vehicle database is obtained based on the combined query. Values of the vehicle record are sent to the client computing device.

Systems are configured for processing user queries to generate search results that are contextually relevant for the user based on customer values associated with a corresponding customer schema associated with the user and that are indexed in a customer value index. When search queries are received, they are processed to identify customer values associated with the user context and to perform an initial search query based on the initial search query terms. The systems also generate additional altered search queries to perform contemporaneously, based on the initial search terms and restructured/reformatted based on the customer values. Resulting supplement search results are obtained for the additional altered search queries, which are merged with the initial search results. The merged results are then ranked and provided to the user. These systems facilitate obtaining search results that are more relevant than results obtained by conventional systems.

Systems are configured for processing user queries to generate search results that are contextually relevant for the user based on customer values associated with a corresponding customer schema associated with the user and that are indexed in a customer value index. When search queries are received, they are processed to identify customer values associated with the user context and to perform an initial search query based on the initial search query terms. The systems also generate additional altered search queries to perform contemporaneously, based on the initial search terms and restructured/reformatted based on the customer values. Resulting supplement search results are obtained for the additional altered search queries, which are merged with the initial search results. The merged results are then ranked and provided to the user. These systems facilitate obtaining search results that are more relevant than results obtained by conventional systems.

Disclosed implementations provide a streamlined, assisted search process for surfacing items from a database that enables guided exploratory searching. For example, a system may receive selected content from a client device and determine a first object and a second object for the selected content using a search converter. The system may generate first results by performing a first search of items in a database using the first object as a query, and generate second results by performing a second search of the items in the database using the second object as a query. The system may select a first set of the first results based on relevance of the first object to the selected content and a second set of the second results based on relevance of the second object to the selected content. The system may provide a combined search result including the first set and the second set.

A query modality recommendation system provides recommendations to use a particular query modality based on one or more categories of search results for a search query. Upon receiving a search query in a first query modality at a search engine, the query modality recommendation system determines to recommend use of a second query modality based on one or more categories of the search results. For example, the first query modality may be a textual query and the second query modality may be an image query. In aspects, recommending use of the second query modality comprises comparing a first search performance of the one or more categories for the first query modality in historical search queries to a second search performance of the one or more categories for the second query modality in the historical search queries.