US 2022/0036197 A1 · COOPERATIVELY TRAINING AND/OR USING SEPARATE INPUT AND SUBSEQUENT CONTENT NEURAL NETWORKS FOR INFORMATION RETRIEVAL

Abstract

Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.

Claims18

Dependent claims

A method implemented by one or more processors, the method comprising:

receiving a textual query generated based on user interface input provided by a user via a client device of the user, the textual query comprising multiple words;
applying a query representation of the textual query to a trained input neural network model, the query representation being based on two or more of the multiple words;
generating one query vector over the trained input neural network model based on applying the query representation to the trained input neural network model;
determining a relevance value that indicates relevance of a content item to the query, wherein determining the relevance value comprises:
determining the relevance value based on a dot product of the query vector to one vector stored in association with the content item, the one vector being stored in association with the content item prior to receiving the query;
based on the relevance value, providing to the client device a result that is based on the content item, the result provided in response to receiving the textual query.

3 more elements

A system, comprising:

memory storing instructions;
one or more processors executing the instructions stored in the memory to cause the one or more processors to:
receive a textual query generated based on user interface input provided by a user via a client device of the user, the textual query comprising multiple words;
apply a query representation of the textual query to a trained input neural network model, the query representation being based on two or more of the multiple words;
generate one query vector over the trained input neural network model based on applying the query representation to the trained input neural network model;
determine a relevance value that indicates relevance of a content item to the query, wherein in determining the relevance value one or more of the processors are to:
determine the relevance value based on a dot product of the query vector to one vector stored in association with the content item, the one vector being stored in association with the content item prior to receiving the query;
based on the relevance value, provide to the client device a result that is based on the content item, the result provided in response to receiving the textual query.

5 more elements

Dependencies are inferred from the claim text.

Cooperatively training and/or using separate input and subsequent content neural networks for information retrieval

Life of this patent

Cooperatively training and/or using separate input and subsequent content neural networks for information retrieval

Life of this patent

Abstract

Claims18