MULTI-PHASE TRAINING OF MACHINE LEARNING MODELS FOR SEARCH RANKING

Aleksandr Boymel, Daria Soboleva

Abstract: A method and system for training a machine learning model to rank digital objects generated using a search query are described. The method includes training the machine learning model in a first phase to determine a predicted user interaction parameter, based on a first plurality of training digital objects associated with past user interaction parameters. The machine learning model is then trained in a second phase to determine a synthetic assessor-generated label, based on a second plurality of training digital objects associated with search queries and labeled with human-assigned assessor-generated labels indicative of a relevance of the training digital objects to the queries. The machine learning model may be applied to the first plurality of training digital objects to generate a first augmented plurality of training digital objects, which may then be used to train the machine learning model to determine a relevance parameter for a digital object.