Abstract: A taxonomy classification system assigns taxonomy labels to content items of an online system. To assign the taxonomy labels, the taxonomy classification system applies one or more taxonomy model to the content items to determine scores or probabilities that a particular label applies to the content item. Each taxonomy model includes multiple sub-models. Each sub-model corresponds to a different type of information for the content item. For example, a first sub-model corresponds to a description of the content item, a second sub-model corresponds to metrics of the content item in one or more content item publishers, a third sub-model corresponds to similar content items to the content item being evaluated. The taxonomy classification system combines the output from every sub-model to determine a label score for one or more labels in a label class and a taxonomy label from the label class is selected based on the determined label score.

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: An analytics system determines the number of downloads for a content item during a first time interval. The system receives engagement data from a plurality of client devices in a panel of client devices. Based on the engagement data, a partial install base for the content item during the first time interval is determined. Based on the determined partial install base for the content item and historical data for other content items, a preliminary install base for the content item during the first time interval is determined. Responsive to determining that the engagement for the content item is greater than a threshold value, an estimated number of downloads for the content item during the first time interval is determined. The estimated number of downloads for the content item during the first time interval is determined based on the preliminary install base for the content item for the first time interval.

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.