Abstract:
A method and apparatus of a device that re-rank a plurality of search results is described. In an exemplary embodiment, the device receives a search query from a user and generates the plurality of search results over a plurality of search domains, wherein the plurality of search results is ranked according to a first ranking. The device additionally generates a re-ranking model, where the re-ranking model includes a plurality of intra-domain models that are generated based on at least based on-device interactions of a plurality of users interacting with a plurality of other devices and each of the plurality of search domains corresponds to one of the plurality of intra-domain models. The device further re-ranks the plurality of search results using the re-ranking model and presents the plurality of search results using the second ranking.
Abstract:
A device implementing a system for providing location based search results includes at least one processor configured to detect that a location of a device is within a location of a store or within a threshold distance of the location, and establish a connection with a wireless network of the store. The at least one processor is configured to access an intranet associated with the store, the intranet being accessed via a network identifier previously stored on the device in association with the store, and receive user input for a search. The at least one processor is configured to obtain, via the intranet, at least one of a query completion suggestion or search result having been targeted to the location of the store or the threshold distance of the location, and display the at least one of the query completion suggestion or the search result in association with the search.
Abstract:
A method and apparatus of a device that generates a re-ranking model used to re-rank a plurality of search results on a client device is described. In an exemplary embodiment, the device receives a crowd-sourced intra-domain model from a server, where the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices. The device further generates a re-ranking model from the crowd-sourced intra-domain model and a local model, where the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results.
Abstract:
A device implementing a system for providing location based search results includes at least one processor configured to detect that a location of a device is within a location of a store or within a threshold distance of the location, and establish a connection with a wireless network of the store. The at least one processor is configured to access an intranet associated with the store, the intranet being accessed via a network identifier previously stored on the device in association with the store, and receive user input for a search. The at least one processor is configured to obtain, via the intranet, at least one of a query completion suggestion or search result having been targeted to the location of the store or the threshold distance of the location, and display the at least one of the query completion suggestion or the search result in association with the search.
Abstract:
A method and apparatus of a device that indexes donated content from an application on a device is described. In an exemplary embodiment, the device receives donated content for an application object from the application. The device further associates a metadata tag for a topic to the donated content. In addition, the device indexes the donated content with the metadata tag in a local search index of the device.
Abstract:
A method and apparatus of a device that generates a re-ranking model used to re-rank a plurality of search results on a client device is described. In an exemplary embodiment, the device receives a crowd-sourced intra-domain model from a server, where the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices. The device further generates a re-ranking model from the crowd-sourced intra-domain model and a local model, where the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results.
Abstract:
A method and apparatus of a device that indexes donated content from an application on a device is described. In an exemplary embodiment, the device receives donated content for an application object from the application. The device further associates a metadata tag for a topic to the donated content. In addition, the device indexes the donated content with the metadata tag in a local search index of the device.
Abstract:
A device implementing a system for limiting the scope of a search includes a processor configured to, receive, by a first application, first user input including at least a portion of a first search term, and to determine that a second application corresponds to the first search term. The processor is further configured to display a graphical element for activating a search filter that limits search results to content of the second application, and to receive second user input including selection of the graphical element. The processor is further configured to send, to the second application, a search request including at least one of the first search term or a second search term, to receive, from the second application, a completion suggestion or search result based on a search performed on the content of the second application, and to provide the completion suggestion or search result.
Abstract:
Methods and systems for client side search ranking improvements are disclosed. In one example, a search query is received from a user on a client device. The local search results are filtered based on the received search query and one or more local ranking rules. Features for each filtered local search result are computed. The computed features of each filtered local search result are input to one or more machine learning (ML) models. Each ML model can generate a score for each filtered local search result. The filtered local search results are ranked based on the generated score within a category. In one example, local search results and remote server search results are obtained. The local search results and remote server search results are ranked using at least one machine learning (ML) ranking model. The ranked local search results and remote server search results are displayed on the client device by category.