Abstract: The present application describes a system and method for searching for content items in an application executing on a computing device. In order to increase the efficiency of the search, the present disclosure provides a refiner that is used to filter or otherwise refine search results. The refiner is user-specific and/or tenant/entity-specific. The refiner may be based on long-term aggregated data and/or contextual information associated with the user.

Abstract: Representative embodiments disclose mechanisms to improve the perceived responsiveness of a search engine. As a user types a query prefix into a browser or other interface to the search engine, the search engine returns query completion suggestions to the browser. The query completion suggestions, user history, user favorites and/or other information are presented to a trained machine learning model on the client device to predict a desired location that the user is attempting to navigate to. When the confidence level of the predicted location surpasses a threshold, content from the desired location is preloaded into a hidden tab in the browser. When the user submits a query, the browser submits feedback to a system responsible for updating and refining the machine learning model. Updated machine learning model coefficients can be received by the browser from the system to make predictions more accurate.

Abstract: The technology described herein provides an efficient mechanism for quickly analyzing huge amounts of media content to find media content (hereafter “content” or “media content”) that is relevant to a user. The technology analyzes features of a curator to classify curators by interest and/or find curators with similar content recommendations. The curator data can be used to make curator recommendations to users based on the user's interests. The technology described herein collects curator data from multiple content sites and analyzes the data to identify curators that recommend similar content on different content sites.

Abstract: Representative embodiments disclose mechanisms to improve the perceived responsiveness of a search engine. As a user types a query prefix into a browser or other interface to the search engine, the search engine returns query completion suggestions to the browser. The query completion suggestions, user history, user favorites and/or other information are presented to a trained machine learning model on the client device to predict a desired location that the user is attempting to navigate to. When the confidence level of the predicted location surpasses a threshold, content from the desired location is preloaded into a hidden tab in the browser. When the user submits a query, the browser submits feedback to a system responsible for updating and refining the machine learning model. Updated machine learning model coefficients can be received by the browser from the system to make predictions more accurate.

Abstract: The technology described herein provides an efficient mechanism for quickly analyzing huge amounts of media content to find media content (hereafter “content” or “media content”) that is relevant to a user. The technology analyzes features of a curator to classify curators by interest and/or find curators with similar content recommendations. The curator data can be used to make curator recommendations to users based on the user's interests. The technology described herein collects curator data from multiple content sites and analyzes the data to identify curators that recommend similar content on different content sites.