Abstract: A server system receives a plurality of comments on a post in an online service, receives feedback on respective comments of the plurality of comments from users of the online service and retrieves feedback weights for the users. The server system ranks the plurality of comments using the feedback and feedback weights and provides the plurality of comments, ordered in accordance with the ranking, for display.

Abstract: Methods, systems, computer-readable media, and apparatuses for optimized survey targeting are presented. In some embodiments, a system comprising one or more processors determines, for each question in a plurality of questions in a survey, a prediction indicative of a likelihood that a first user will provide a specific answer to the question and a certainty score associated with the prediction. The system may then identify, based upon the predictions and the certainty scores determined for the plurality of questions in the survey, a first subset of questions from the plurality of questions in the survey, the identifying comprising excluding one or more questions in the plurality of questions from the first subset of questions. The system may also present, a first question from the first subset of questions to the first user.

Abstract: A client relationship management (CRM) application can generate a ranked list of client engagement tools by computing a rank score for available client engagement tools and determining an order among the available client engagement tools based on the rank scores. The CRM application can use one or more trained prediction models and business rules to compute a prediction for success for client engagement tools. For example, the CRM application can use three prediction models, one predicting user selection of a client engagement tool from a list of available client engagement tools; one predicting a user's adopting the recommendation in a client engagement tool; and one predicting a client approving a client engagement tool recommendation. The predictions for success can be combined with estimated benefit values for the client engagement tool to compute the client engagement tool rank score.

Abstract: A server system receives a plurality of comments on a post in an online service, receives feedback on respective comments of the plurality of comments from users of the online service and retrieves feedback weights for the users. The server system ranks the plurality of comments using the feedback and feedback weights and provides the plurality of comments, ordered in accordance with the ranking, for display.

Abstract: Systems, methods, and non-transitory computer readable media can obtain a plurality of change points that are each indicative of a potential change in a curve relating to a metric associated with a system. A prediction model for providing forecasts relating to the metric can be generated. A seasonality model for predicting seasonality associated with the metric can be generated. A combined forecast model can be generated based on the prediction model and the seasonality model.

Abstract: Systems, methods, and non-transitory computer-readable media train a machine learning model to forecast growth of a content item, the growth being measured based at least in part on a count of user interactions with the content item, wherein the model is trained to adjust growth forecasts for the content item in response to one or more users interacting with the content item. A first growth forecast for the content item can be determined for a unit of time using the machine learning model. A determination is made that a first user has interacted with the content item. A second growth forecast for the content item can be determined for the unit of time using the machine learning model and based at least in part on the first user interacting with the content item.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a time series data set. At least one simulated forecast time period for the time series data set can be determined. One or more parameters for generating one or more forecasting models for the time series data set can be determined. The one or more forecasting models can be generated based at least in part on the one or more parameters and on the at least one simulated forecast time period. The one or more forecasting models can be evaluated to determine an optimal forecasting model for the time series data set.