Index-based technique friendly CTR prediction and advertisement selection

摘要

Methods and systems are provided for click through rate prediction and advertisement selection in online advertising. Methods are provided in which output information from a feature-based machine learning model is utilized. The output information includes predicted click through rate information. The output information is used to form a matrix. The matrix is modeled using a latent variable model. Machine learning techniques can be used in determining values for unfilled cells of one or more model matrices. The latent variable model can be used in determining predicted click through rate information, and in advertisement selection in connection with serving opportunities.