US 2020/0401931 A1 · LOCATION DIMENSION REDUCTION USING GRAPH TECHNIQUES

Abstract

Technologies for generating a graph containing clusters of feature attribute values for training a machine learning model for content item selection and delivery are provided. The disclosed techniques include, for each entity, of a plurality of entities, a system identifies transitions from one geographic location to another geographic location. A graph is generated based on the transitions associated with each entity. The graph comprises nodes representing geographic locations and edges connecting the nodes. Each of the edges connects two nodes, represents a transition from one geographic location to another geographic location, and each edge represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two connected nodes. The system generates a plurality of clusters from the nodes based upon the edge weight value of each edge. The system includes the plurality of clusters as features in a machine learning model.

Claims20

Dependent claims

A method comprising:

for each entity of a plurality of entities, identifying one or more transitions from one geographic location to another geographic location;
generating a graph based on the one or more transitions associated with each entity of the plurality of entities;
wherein the graph comprises a plurality of nodes representing geographic locations and a plurality of edges connecting the plurality of nodes;
wherein each edge of the plurality of edges (1) connects two nodes of the plurality of nodes, (2) represents a transition from a geographic location represented by one of the two nodes to the other of the two nodes, and (3) represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two nodes;
generating a plurality of clusters from the plurality of nodes based on the edge weight value of each edge of the plurality of edges;
and
including the plurality of clusters as feature values in a machine-learned model;
wherein the method is performed by one or more computing devices.

5 more elements

A computer program product comprising:

one or more non-transitory computer-readable storage media comprising instructions which, when executed by one or more processors, cause:
for each entity of a plurality of entities, identifying one or more transitions from one geographic location to another geographic location;
generating a graph based on the one or more transitions associated with each entity of the plurality of entities;
wherein the graph comprises a plurality of nodes representing geographic locations and a plurality of edges connecting the plurality of nodes;
wherein each edge of the plurality of edges (1) connects two nodes of the plurality of nodes, (2) represents a transition from a geographic location represented by one of the two nodes to the other of the two nodes, and (3) represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two nodes;
generating a plurality of clusters from the plurality of nodes based on the edge weight value of each edge of the plurality of edges;
and
including the graph and the plurality of clusters as feature values in a machine-learned model.

5 more elements

A system comprising:

one or more computer processors;
an optimized graph generation system coupled to the one or more processors, wherein the optimized graph generation system performs operations comprising:
for each entity of a plurality of entities, identifying one or more transitions from one geographic location to another geographic location;
generating a graph based on the one or more transitions associated with each entity of the plurality of entities;
wherein the graph comprises a plurality of nodes representing geographic locations and a plurality of edges connecting the plurality of nodes;
wherein each edge of the plurality of edges (1) connects two nodes of the plurality of nodes, (2) represents a transition from a geographic location represented by one of the two nodes to the other of the two nodes, and (3) represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two nodes;
generating a plurality of clusters from the plurality of nodes based on the edge weight value of each edge of the plurality of edges;
and
including the graph and the plurality of clusters as feature values in a machine-learned model.

6 more elements

Dependencies are inferred from the claim text.

Location dimension reduction using graph techniques

Life of this patent

Location dimension reduction using graph techniques

Life of this patent

Abstract

Claims20