Granted Patent B2

US 11,436,656 · App. 15/074,922 · Granted Sep 6, 2022

Inventors: Eric Huang (San Francisco, CA); Rong Zhou (San Jose, CA)

Assignee: Palo Alto Research Center Incorporated

G06Q30/0631G06Q30/0643

Abstract

Claims (80)

1. A computer-executable method for generating a product recommendation, comprising:

retrieving a dataset comprising information associated with consumers and products from a database running on a database management system (DBMS) that supports execution of a query language;

generating graph data indicating vertices and edges of a graph from the retrieved dataset, wherein the vertices represent the information associated with the consumers and products and an edge represents an access relationship between a consumer and a product indicated in the dataset;

receiving a database query, which is expressed based on the query language, at the database to determine a product recommendation based on the information in the database;

converting the database query to a set of graph operation primitives of a graph solver, wherein the set of graph operation primitives are expressed based on the graph and mapped to a set of database operations associated with the database query;

performing the set of graph operation primitives on the graph using the graph solver to determine a set of vertices representing products; and

generating a recommendation based on the set of vertices obtained from the graph solver as a response to the database query.

2. The method of claim 1 , further comprising:

storing the graph data in one or more database tables of a graph database, which is distinct from the database, running on the DBMS; and

streaming the graph data from the graph database over a network to an executing application.

3. The method of claim 1 , further comprising:

receiving command-line parameters indicating a number of starting vertices, a number of recommendations to be generated, a number of traversals, or a number of media that must be shared in common between an ego associated with the database query and another consumer.

4. The method of claim 1 , further comprising:

receiving a second database query, which is expressed based on the query language, at the database and determining recommendations for a plurality of egos based on the second database query;

determining, by a plurality of processors operating in parallel, a second set of graph operation primitives, which are expressed based on the graph, of the graph solver associated with the plurality of egos, wherein the second set of graph operation primitives are expressed based on the graph and mapped to a second set of database operations associated with the second database query;

performing the second set of graph operation primitives on the graph using the graph solver such that to determine a second set of vertices representing other products are determined;

calculating a tally for each of the second set of vertices;

sorting the second set of vertices; and

generating a recommendation based on the sorted second set of vertices.

5. The method of claim 1 , wherein the graph includes multiple edge types of the edges of the graph, wherein one of the edge types represents a like relationship between a respective consumer and a corresponding product, and wherein a vertex representing an ego associated with the database query and a plurality of vertices representing other consumers of products associated with the ego are connected via edges representing the like relationship.

6. The method of claim 1 , further comprising:

calculating a tally for each of the set of vertices.

7. The method of claim 6 , wherein generating the recommendation further comprises:

sorting and ranking the set of vertices; and

generating the recommendation to include one of:

all products associated with the set of vertices and their corresponding tallies;

products associated with a subset of the set of vertices with tallies that are above a predetermined ranking; and

products associated with a subset of the set of vertices with tallies that are greater than a predetermined user-specified value.

8. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for generating a product recommendation, the method comprising:

retrieving a dataset comprising information associated with consumers and products from a database running on a database management system (DBMS) that supports execution of a query language;

generating graph data indicating vertices and edges of a graph from the retrieved dataset, wherein the vertices represent the information associated with the consumers and products and an edge represents an access relationship between a consumer and a product indicated in the dataset;

receiving a database query, which is expressed based on the query language, at the database to determine a product recommendation based on the information in the database;

converting the database query to a set of graph operation primitives of a graph solver, wherein the set of graph operation primitives are expressed based on the graph and mapped to a set of database operations associated with the database query;

performing the set of graph operation primitives on the graph using the graph solver to determine a set of vertices representing products; and

generating a recommendation based on the set of vertices obtained from the graph solver as a response to the database query.

9. The computer-readable storage medium of claim 8 , wherein the method further comprises:

storing the graph data in one or more database tables of a graph database, which is distinct from the database, running on the DBMS; and

streaming the graph data from the graph database over a network to an executing application.

10. The non-transitory computer-readable storage medium of claim 8 , wherein the method further comprises:

receiving command-line parameters indicating a number of starting vertices, a number of recommendations to be generated, a number of traversals, or a number of media that must be shared in common between an ego associated with the database query and another consumer.

11. The non-transitory computer-readable storage medium of claim 8 , wherein the method further comprises:

receiving a second database query, which is expressed based on the query language, at the database and determining product recommendations for a plurality of egos based on the second database query;

determining, by a plurality of processors operating in parallel, a second set of graph operation primitives of the graph solver associated with the plurality of egos, wherein the second set of graph operation primitives, are expressed based on the graph and mapped to a second set of database operations associated with the second database query;

performing the second set of graph operation primitives on the graph using the graph solver such that a second set of vertices representing other products are determined;

calculating a tally for each of the second set of vertices;

sorting the second set of vertices; and

generating a recommendation based on the sorted second set of vertices.

12. The non-transitory computer-readable storage medium of claim 8 , wherein the graph includes multiple edge types of the edges of the graph, wherein one of the edge types represents a like relationship between a respective consumer and a corresponding product, and wherein a vertex representing an ego associated with the database query and a plurality of vertices representing other consumers of products associated with the ego are connected via edges representing the like relationship.

13. The non-transitory computer-readable storage medium of claim 8 , wherein the method further comprises:

calculating a tally for each of the set of vertices.

14. The non-transitory computer-readable storage medium of claim 13 , wherein generating the recommendation further comprises:

sorting and ranking the set of vertices; and

generating the recommendation to include one of:

all products associated with the set of vertices and their corresponding tallies;

products associated with a subset of the set of vertices with tallies that are above a predetermined ranking; and

products associated with a subset of the set of vertices with tallies that are greater than a predetermined user-specified value.

15. A computing system for generating a product recommendation, the system comprising:

one or more processors,

a non-transitory computer-readable medium coupled to the one or more processors having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform a method for generating a product recommendation, the method comprising:

retrieving a dataset comprising information associated with consumers and products from a database running on a database management system (DBMS) that supports execution of a query language;

generating graph data indicating vertices and edges of a graph from the retrieved dataset, wherein the vertices represent the information associated with the consumers and products and an edge represents an access relationship between a consumer and a product indicated in the dataset;

receiving a database query, which is expressed based on the query language, at the database to determine a product recommendation based on the information in the database;

converting the database query to a set of graph operation primitives of a graph solver, wherein the set of graph operation primitives are expressed based on the graph and mapped to a set of database operations associated with the database query;

performing the set of graph operation primitives on the graph using the graph solver to determine a set of vertices representing products; and

generating a recommendation based on the set of vertices obtained from the graph solver as a response to the database query.

16. The computing system of claim 15 , wherein the method further comprises:

storing the graph data in one or more database tables of a graph database, which is distinct from the database, running on the DBMS; and

streaming the graph data from the graph database over a network to an executing application.

17. The computing system of claim 15 , wherein the method further comprises:

receiving command-line parameters indicating a number of starting vertices, a number of recommendations to be generated, a number of traversals, or a number of media that must be shared in common between an ego associated with the database query and another consumer.

18. The computing system of claim 15 , wherein the method further comprises:

receiving a second database query, which is expressed based on the query language, at the database and determining product recommendations for a plurality of egos based on the second database query;

determining, by a plurality of processors operating in parallel, a second set of graph operation primitives, which are expressed based on the graph, of the graph solver associated with the plurality of egos, wherein the second set of graph operation primitives are expressed based on the graph and mapped to a second set of database operations associated with the second database query;

performing the second set of graph operation primitives on the graph using the graph solver such that a second set of vertices representing other products are determined;

calculating a tally for each of the second set of vertices;

sorting the second set of vertices; and

generating a recommendation based on the sorted second set of vertices.

19. The computing system of claim 15 , wherein the graph includes multiple edge types of the edges of the graph, wherein one of the edge types represents a like relationship between a respective consumer and a corresponding product, and wherein a vertex representing an ego associated with the database query and a plurality of vertices representing other consumers of products associated with the ego are connected via edges representing the like relationship.

20. The computing system of claim 15 , wherein the method further comprises:

calculating a tally for each of set of vertices.

Assignments (9)

Continuity (1)

Cited By (3)

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