MULTISTAGE TRAINING OF MACHINE LEARNING MODELS FOR RANKING SEARCH RESULTS Russian patent published in 2024 - IPC G06N3/08 G06F16/00 

FIELD: physics.

SUBSTANCE: invention relates to a system and a method for training a machine learning model to rank digital objects of the use stage. Method includes obtaining by processor a first plurality of training digital objects, wherein each training digital object from the first plurality of training digital objects is associated with a parameter of past user actions indicating user actions of past users with said training digital object; training at the first stage of training based on the first set of training digital objects of the machine learning model to determine the parameter of predicted user actions for the digital object of the use stage, wherein the predicted user actions parameter indicates user actions of future users with the digital object of the use stage; obtaining by the processor a second set of training digital objects, wherein each training digital object from the second set of training digital objects is connected (a) with a training search query used to generate a training digital object from a second plurality of training digital objects, and (b) with a first label indicating the degree of relevance of the object from the second plurality of training digital objects to the training search query; training at the second training stage following the first training stage, based on the second plurality of training digital objects of the machine learning model, determining a synthesized label of the digital object of the use stage, indicating the degree of relevance of the digital object of the use stage to the search request of the use stage; application by a processor of a machine learning model with respect to a first plurality of training digital objects to augment an object from the first plurality of training digital objects with a synthesized label and thus forming a first augmented plurality of training digital objects; and training, based on the first augmented set of training digital objects of the machine learning model, to determine the relevance parameter of the digital object of the use stage, which indicates the degree of relevance of the digital object of the use stage to the search request of the use stage, wherein the training digital object from the first plurality of training digital objects contains an indication of the digital document associated with the document metadata, and based on the first plurality of training digital objects, training the machine learning model at the first training stage further includes: converting the document metadata into a text representation thereof containing tokens; preprocessing the text representation for masking several masked tokens therein; and training, based on the first plurality of training digital objects, of the machine learning model to determine a token from a plurality of masked tokens based on the context provided by neighboring tokens, wherein the relevance parameter of the digital object of the use stage further indicates a semantic relevance parameter indicating the degree of semantic relevance of the search query of the use stage to the content of the digital object of the use stage.

EFFECT: high relevance of search results generated by a search engine in response to a user request, due to accurate ranking of search results on a SERP page, performed by a machine learning model.

23 cl, 6 dwg