US 2020/0005153 A1Published application

Unsupervised learning of entity representations using graphs

AssigneeMicrosoft Technology Licensing, LLC

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

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Abstract

Techniques for implementing a learning semantic representations of sparse entities using unsupervised embeddings are disclosed herein. In some embodiments, a computer system accesses corresponding profile data of users indicating at least one entity of a first facet type associated with the user, and generating a graph data structure comprising nodes and edges based on the accessed profile data, with each node corresponding to a different entity indicated by the accessed profile data, and each edge directly connecting a different pair of nodes and indicating a number of users whose profile data indicates both entities of the pair of nodes. The computer system generating a corresponding embedding vector for the entities based on the graph data structure using an unsupervised machine learning algorithm.

Claims20

Dependent claims

A computer-implemented method comprising:

for each one of a plurality of users of an online service, accessing, by a computer system having a memory and at least one hardware processor, corresponding profile data of the user stored on a database of the online service, the accessed profile data of each user indicating at least one entity of a first facet type associated with the 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 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 embedding vector for each one of the entities indicated by the accessed profile data based on the graph data structure using an unsupervised machine learning algorithm;
and
performing, by the computer system, a function of the online service using the generated embedding vectors of the entities.

2 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 users of an online service, accessing corresponding profile data of the user stored on a database of the online service, the accessed profile data of each user indicating at least one entity of a first facet type associated with the 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 users whose profile data indicates both entities of the pair of nodes that are directly connected by the edge;
generating a corresponding embedding vector for each one of the entities indicated by the accessed profile data based on the graph data structure using an unsupervised machine learning algorithm;
and
performing a function of the online service using the generated embedding vectors of the entities.

5 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 users of an online service, accessing corresponding profile data of the user stored on a database of the online service, the accessed profile data of each user indicating at least one entity of a first facet type associated with the 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 users whose profile data indicates both entities of the pair of nodes that are directly connected by the edge;
generating a corresponding embedding vector for each one of the entities indicated by the accessed profile data based on the graph data structure using an unsupervised machine learning algorithm;
and
performing a function of the online service using the generated embedding vectors of the entities.

2 more elements

Dependencies are inferred from the claim text.