Abstract: A system and method for recommending targeting attributes for advertising campaigns are provided. The system and method comprise receiving at least one advertizing campaign, receiving historical targeting data and historical click-through data, selecting a machine learning model, and determining recommended targeting attributes for the advertising campaign using the machine learning model and the received data. The machine learning model may be a collaborative filtering model or a performance oriented model. The collaborative filtering model may rely on matrix factorization or Boltzmann Machines. The performance-oriented model may rely on a local regression.

Abstract: The subject matter disclosed herein relates to cross-lingual query classification.

Abstract: Systems and methods for estimating an amount of traffic associated with a digital ad are disclosed. Generally, a forecasting module identifies a set of candidate webpages on which a digital ad may be displayed and estimates a click through rate associated with the digital ad and a webpage of the set of candidate webpages. The forecasting module determines a ranking score associated with the digital ad based on the determined click through rate and a bid price associated with the digital ad. The forecasting module then examines historical data, such as search logs, to determine an estimate of traffic associated with the digital ad with respect to the webpage in response to determining the ranking score of the digital ad exceeds a ranking score associated with another digital ad that was previously displayed on the webpage.