Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive data and associated relevance information from a data source and a set of signals describing a current environment of a user or historical user behavior information in which the data source being local to a computing device. The processor may be further configured to provide, using a machine learning model, a relevance score for each of multiple data items based at least in part on the received relevance information and the set of signals. The processor may be further configured to sort the data items based on a ranking of each relevance score for each data item. The processor may be further configured to provide, as output, the multiple data items based at least in part on the ranking.

Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive data and associated relevance information from a data source and a set of signals describing a current environment of a user or historical user behavior information in which the data source being local to a computing device. The processor may be further configured to provide, using a machine learning model, a relevance score for each of multiple data items based at least in part on the received relevance information and the set of signals. The processor may be further configured to sort the data items based on a ranking of each relevance score for each data item. The processor may be further configured to provide, as output, the multiple data items based at least in part on the ranking.

Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive application data from one or more applications, the application data including features related to user activity from the one or more applications, the one or more application including applications local to the device that are stored in the memory and applications external to the device. The processor may be further configured to provide, using a machine learning (ML) model, a relevance score for each of one or more user interface (UI) elements based on each of the features. The processor may be further configured to sort one or more UI elements based on a ranking of the relevance scores. The processor may be further configured to provide, as output, the one or more UI elements based at least in part on the ranking.

Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive application data from one or more applications, the application data including features related to user activity from the one or more applications, the one or more application including applications local to the device that are stored in the memory and applications external to the device. The processor may be further configured to provide, using a machine learning (ML) model, a relevance score for each of one or more user interface (UI) elements based on each of the features. The processor may be further configured to sort one or more UI elements based on a ranking of the relevance scores. The processor may be further configured to provide, as output, the one or more UI elements based at least in part on the ranking.

Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive data and associated relevance information from a data source and a set of signals describing a current environment of a user or historical user behavior information in which the data source being local to a computing device. The processor may be further configured to provide, using a machine learning model, a relevance score for each of multiple data items based at least in part on the received relevance information and the set of signals. The processor may be further configured to sort the data items based on a ranking of each relevance score for each data item. The processor may be further configured to provide, as output, the multiple data items based at least in part on the ranking.

Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive data and associated relevance information from a data source and a set of signals describing a current environment of a user or historical user behavior information in which the data source being local to a computing device. The processor may be further configured to provide, using a machine learning model, a relevance score for each of multiple data items based at least in part on the received relevance information and the set of signals. The processor may be further configured to sort the data items based on a ranking of each relevance score for each data item. The processor may be further configured to provide, as output, the multiple data items based at least in part on the ranking.

Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive application data from one or more applications, the application data including features related to user activity from the one or more applications, the one or more application including applications local to the device that are stored in the memory and applications external to the device. The processor may be further configured to provide, using a machine learning (ML) model, a relevance score for each of one or more user interface (UI) elements based on each of the features. The processor may be further configured to sort one or more UI elements based on a ranking of the relevance scores. The processor may be further configured to provide, as output, the one or more UI elements based at least in part on the ranking.

Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive data and associated relevance information from a data source and a set of signals describing a current environment of a user or historical user behavior information in which the data source being local to a computing device. The processor may be further configured to provide, using a machine learning model, a relevance score for each of multiple data items based at least in part on the received relevance information and the set of signals. The processor may be further configured to sort the data items based on a ranking of each relevance score for each data item.