Search relevance model using self-adversarial negative sampling

A product comprising a query-item relevance model stored on a non-transitory computer readable storage medium, wherein the query-item relevance model is manufactured by a process comprising:

• obtaining a training dataset that comprises a plurality of training examples, wherein each of the plurality of training examples is either a positive training example or a negative training example, and wherein each of the plurality of training examples comprises:
• a query comprising one or more terms, and
• item data describing one or more attributes of an item;
• adding a label to each of the plurality of training examples, wherein:
• the label for a positive training example indicates that a specific interaction was performed with the item after the query was received, and
• the label for a negative training example indicates that a specific interaction was not performed with the item after the query was received, the label further weighted by a measure of similarity between the item of the training example and the query of the training example;
• and
• accessing a machine learning model comprising a network of a plurality of layers, the machine learning model configured to receive an input query and an input item and to generate a measure of relevance of the input item to the input query;
• for each training example of the plurality of training examples of the training dataset:
• applying the machine learning model to the query of the training example and to the item of the training example, wherein the machine learning model outputs a measure of relevance based thereon,
• generating an error term based on a difference between the measure of relevance output from the machine learning model and the label of the training example, and
• backpropagating the error term to update a set of parameters of the machine learning model;
• and
• storing the set of parameters on a non-transitory computer readable storage medium as trained parameters of the query-item relevance model.

A system for generating a query-item relevance model, the system comprising:

• one or more processors;
• and
• a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the one or more processors, cause the one or more processors to perform steps comprising:
• obtaining a training dataset that comprises a plurality of training examples, wherein each of the plurality of training examples is either a positive training example or a negative training example, and wherein each of the plurality of training examples comprises:
• a query comprising one or more terms, and
• item data describing one or more attributes of an item;
• adding a label to each of the plurality of training examples, wherein:
• the label for a positive training example indicates that a specific interaction was performed with the item after the query was received, and
• the label for a negative training example indicates that a specific interaction was not performed with the item after the query was received, the label further weighted by a measure of similarity between the item of the training example and the query of the training example;
• and
• accessing a machine learning model comprising a network of a plurality of layers, the machine learning model configured to receive an input query and an input item and to generate a measure of relevance of the input item to the input query;
• for each training example of the plurality of training examples of the training dataset:
• applying the machine learning model to the query of the training example and to the item of the training example, wherein the machine learning model outputs a measure of relevance based thereon,
• generating an error term based on a difference between the measure of relevance output from the machine learning model and the label of the training example, and
• backpropagating the error term to update a set of parameters of the machine learning model;
• and
• storing the set of parameters on a non-transitory computer readable storage medium as trained parameters of the query-item relevance model.