Abstract: A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.

Abstract: A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.

Abstract: A network based advertisement system includes an optimizer configured to forecast a supply of opportunities, forecast a supply of guaranteed contracts, and forecast a supply of non-guaranteed contracts. Each opportunity represents a user visiting a webpage. Each guaranteed contract guarantees the matching of an advertisement to a number of opportunities. Each non-guaranteed contract guarantees a user event associated with an advertisement. The optimizer then generates a plan for matching contracts to opportunities based on the forecasted supply of opportunities, the forecasted supply of guaranteed contracts, the forecasted supply of non-guaranteed contracts, and an objective function that balances a group of parameters that define the representativeness of contracts, a cost associated with not serving non-guaranteed contracts, and performance objectives associated with contracts.

Abstract: A computer-implemented method for matching a display advertisement to a user within a large-scale, non-destructive user modeling and experimentation environment using real-time traffic. The method commences by populating a user profile object (containing demographics, history, and behaviors of the user) for use during concurrent operation of a production platform and an experimentation platform. To implement non-destructive testing, the method continues by cloning a portion of the real-time traffic for use by the experimentation platform while concurrently delivering the real-time traffic to the production platform. The production platform and the experimentation platform operate concurrently, scoring matches between the user profile objects and a plurality of display advertisements for selecting among the best-scored advertisements.

Abstract: A method for making recommendations to improve advertisement campaign performance includes receiving a seed campaign insertion order (IO) having one or more campaign IO lines; computing a plurality of neighbor ad campaigns based on a comparison of the seed campaign IO with a dataset of advertiser ad campaign IO lines; generating campaign IO recommendations by executing an algorithm to recommend a campaign feature and value (FV) as a change to a line of the seed campaign IO based on success of such use by the neighbor ad campaigns; ranking the FV recommendations based on at least one performance metric; filtering the FV recommendations based on a plurality of performance-enhancing criteria of the seed campaign IO and the neighbor ad campaigns with respect to individual FV recommendations; and displaying the ranked FV recommendations to the advertiser for selection.

Abstract: A method for recommending improvements to advertisement campaign performance includes receiving a seed campaign insertion order (IO) having one or more campaign IO lines; computing a plurality of neighbor ad campaigns based on comparison of the seed campaign IO with a dataset of advertiser ad campaign IO lines; generating campaign IO recommendations by executing an algorithm to recommend profiles to add to the seed campaign IO from booking lines corresponding to the profiles based on performance of such use by the neighbor ad campaigns being generally above average when compared with campaigns that did not use the recommended profiles; filtering the profile recommendations based on a plurality of performance-enhancing criteria of the seed campaign IO and the neighbor ad campaigns with respect to each potential profile recommendation; ranking the profile recommendations based on at least one performance metric; and displaying the ranked profile recommendations to the advertiser for selection.