Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: A graph includes nodes representing applications and tags describing subjective qualities of the applications. The system generates tags for applications using a large language model (LLM). The system receives the name of an application and generates a prompt for an LLM based on the name. The prompt includes a request for one or more tags associated with the application. The system provides the prompt to the LLM for execution and receives, as output from the LLM, candidate tags. The system inputs the candidate tags into a classifier trained to classify candidate tags into known tags, tags that already exist in a graph. The system receives, as output from the classifier, known tags. The system updates the graph to include a node corresponding to the application, the node linked to the known tags with one or more edges.

Abstract: A graph includes nodes representing applications and tags describing subjective qualities of the applications. The system responds to queries for user personas by using an LLM to match the persona to applications in the graph. The system receives a natural language query describing a persona. The system generates a prompt for an LLM based on the query and provides the prompt to the LLM for execution. The system receives, as output from the LLM, candidate applications. The system inputs the candidate applications into a classifier trained to classify candidate applications into known applications, applications that already exist in a graph. The system receives, as output from the classifier, known applications. The system determines, for each known application, a quality score of the known application and determines that the quality score exceeds a quality score threshold. In response, the system provides the known applications for display at a user interface.

Abstract: A graph may be initially seeded with nodes representing applications and tags describing subjective qualities of the applications. A system generates tags for new applications using a supervised machine learning model. The system extracts signals from a newly detected application. The system inputs the signals into a machine learning model, and receives, as output from the model, tags that correspond to the new application and levels of confidence for each tag. The system updates the graph to include one or more nodes corresponding to the new application, with the tags linked to the one or more nodes with an edge that has a weight corresponding to the level of confidence. The system receives a query corresponding to the tag and provides a response to the query based on the one or more nodes corresponding to the new application.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.