US 2020/0005134 A1Published application

Generating supervised embeddings using unsupervised embeddings

AssigneeMicrosoft Technology Licensing, LLC

InventorsRohan Ramanath, Gungor Polatkan, Qi Guo, Cagri Ozcaglar, Krishnaram Kenthapadi, Sahin Cem Geyik

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Abstract

Techniques for generating supervised embedding representations using unsupervised embedding representations and deep semantic structured models for search are disclosed herein. In some embodiments, a computer system generates a graph data structure based on accessed profile data, generates an initial embedding vector using an unsupervised machine learning algorithm, receiving training data comprising query representations, search result representations, and user actions, with each one of the plurality of query representations comprising the initial embedding vector, and generates a final embedding vector using a supervised learning algorithm and the received training data.

Claims20

Dependent claims

A computer-implemented method comprising:

for each one of a plurality of reference users of an online service, accessing, by a computer system having a memory and at least one hardware processor, corresponding profile data of the reference user stored on a database of the online service, the accessed profile data of each reference user indicating at least one entity of a first facet type associated with the reference user;
generating, by the computer system, a graph data structure based on the accessed profile data, the generated graph data structure comprising a plurality of nodes and a plurality of edges, each one of the plurality of nodes corresponding to a different entity indicated by the accessed profile data, and each one of the plurality of edges directly connecting a different pair of the plurality of nodes and indicating a number of the plurality of reference users whose profile data indicates both entities of the pair of nodes that are directly connected by the edge;
generating, by the computer system, a corresponding initial embedding vector for each one of the entities indicated by the accessed profile data using an unsupervised machine learning algorithm;
receiving, by the computer system, training data comprising a plurality of query representations, a plurality of search result representations for each one of the plurality of query representations, and a plurality of user actions for each one of the plurality of query representations, each one of the plurality of query representations comprising the corresponding initial embedding vector of at least one entity included in a corresponding search query submitted by a querying user, the corresponding plurality of search result representations for each one of the plurality of query representations representing a plurality of candidate users displayed in response to the plurality of search queries based on profile data of the plurality of candidate users stored on the database of the online service, the plurality of user actions comprising actions by the querying user directed towards at least one candidate user of the plurality of search results for the corresponding search query;
generating, by the computer system, a corresponding final embedding vector for each one of the at least one entity using a supervised learning algorithm and the received training data;
and
performing, by the computer system, a function of the online service using the generated final embedding vector for each one of the at least one entity.

4 more elements

A system comprising:

at least one hardware processor;
and
a non-transitory machine-readable medium embodying a set of instructions that, when executed by at least one hardware processor, cause the processor to perform operations comprising:
for each one of a plurality of reference users of an online service, accessing corresponding profile data of the reference user stored on a database of the online service, the accessed profile data of each reference user indicating at least one entity of a first facet type associated with the reference user;
generating a graph data structure based on the accessed profile data, the generated graph data structure comprising a plurality of nodes and a plurality of edges, each one of the plurality of nodes corresponding to a different entity indicated by the accessed profile data, and each one of the plurality of edges directly connecting a different pair of the plurality of nodes and indicating a number of the plurality of reference users whose profile data indicates both entities of the pair of nodes that are directly connected by the edge;
generating a corresponding initial embedding vector for each one of the entities indicated by the accessed profile data using an unsupervised machine learning algorithm;
receiving training data comprising a plurality of query representations, a plurality of search result representations for each one of the plurality of query representations, and a plurality of user actions for each one of the plurality of query representations, each one of the plurality of query representations comprising the corresponding initial embedding vector of at least one entity included in a corresponding search query submitted by a querying user, the corresponding plurality of search result representations for each one of the plurality of query representations representing a plurality of candidate users displayed in response to the plurality of search queries based on profile data of the plurality of candidate users stored on the database of the online service, the plurality of user actions comprising actions by the querying user directed towards at least one candidate user of the plurality of search results for the corresponding search query;
generating a corresponding final embedding vector for each one of the at least one entity using a supervised learning algorithm and the received training data;
and
performing a function of the online service using the generated final embedding vector for each one of the at least one entity.

7 more elements

A non-transitory machine-readable medium embodying a set of instructions that, when executed by at least one hardware processor, cause the processor to perform operations comprising:

for each one of a plurality of reference users of an online service, accessing corresponding profile data of the reference user stored on a database of the online service, the accessed profile data of each reference user indicating at least one entity of a first facet type associated with the reference user;
generating a graph data structure based on the accessed profile data, the generated graph data structure comprising a plurality of nodes and a plurality of edges, each one of the plurality of nodes corresponding to a different entity indicated by the accessed profile data, and each one of the plurality of edges directly connecting a different pair of the plurality of nodes and indicating a number of the plurality of reference users whose profile data indicates both entities of the pair of nodes that are directly connected by the edge;
generating a corresponding initial embedding vector for each one of the entities indicated by the accessed profile data using an unsupervised machine learning algorithm;
receiving training data comprising a plurality of query representations, a plurality of search result representations for each one of the plurality of query representations, and a plurality of user actions for each one of the plurality of query representations, each one of the plurality of query representations comprising the corresponding initial embedding vector of at least one entity included in a corresponding search query submitted by a querying user, the corresponding plurality of search result representations for each one of the plurality of query representations representing a plurality of candidate users displayed in response to the plurality of search queries based on profile data of the plurality of candidate users stored on the database of the online service, the plurality of user actions comprising actions by the querying user directed towards at least one candidate user of the plurality of search results for the corresponding search query;
generating a corresponding final embedding vector for each one of the at least one entity using a supervised learning algorithm and the received training data;
and
performing a function of the online service using the generated final embedding vector for each one of the at least one entity.

4 more elements

Dependencies are inferred from the claim text.