Characterizing an entity in an identifier space based on behaviors of unrelated entities in a different identifier space

• US 9,330,209 B1
• Filed: 07/09/2013
• Issued: 05/03/2016
• Est. Priority Date: 07/09/2013
• Status: Active Grant

• Current Assignee: Quantcast
• Original Assignee: Quantcast
• Inventors: Kamprath, Michael F., McCormick, Sean, Murff, Scott Michael
• Primary Examiner(s): Gebresilassie, Kibrom K
• Application Number:
  US13/937,864
• Time in Patent Office: 1,029 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0276 Advertisement creation
• CPC Class Codes: G06Q 30/0277 Online advertisement
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04N 21/4622 Retrieving content or additional data from different sources, e.g. from a broadcast channel and the Internet web site content organization and management for information retrieval from the Internet G06F16/958; transmission by internet of broadcast information H04H60/82; stock exchange data over packet-switching network H04L12/1804; push services including data channel over packet-switching network H04L12/1859
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

Models are built based on existing histories in one identifier space to infer features of entities in a different identifier space. A source model is built using features of an archetypical population in a given identifier space and the standard population. A join panel, i.e., a set of entities operating across both the given identifier space and a second disjoined identifier space, is scored using the source model. Based on the scores and features associated with the entities in the join panel within the second identifier space, a target model specific to the second identifier space is built. An audience of entities within the second identifier space can then be scored using the target model to identify entities that are similar to the archetypical population.

Claims

1. A computer-implemented method of determining the similarity between entities across different identifier spaces, the method comprising:

building a first model specific to a first identifier space based at least on a first set of features associated with an archetypical population operating in the first identifier space, the first set of features determined based on histories associated with the archetypical population in the first identifier space;

identifying a join panel of entities that each operates in both the first identifier space and a second identifier space, each entity of the join panel having a respective first identifier associated with the first identifier space mapped to a respective second identifier associated with the second identifier space;

computing a score for each entity of the join panel by applying the first model to each entity of the join panel, each respective score reflective of the similarity between the respective entity of the join panel and the archetypical population;

identifying a second set of features associated with the entities of the join panel based on the scores, the second set of features determined based on histories associated with the entities of the join panel in the second identifier space;

building a second model specific to the second identifier space based at least on the second set of features;

applying the second model to a target entity operating in the second identifier space to compute a score for the target entity, the score indicating the similarity between the target entity operating in the second identifier space and the archetypical population operating in the first identifier space, wherein an identifier associated with the target entity in the second identifier space is not mapped to an identifier in the first identifier space;

responsive to the score for the target entity indicating the target entity is similar to the archetypical population, targeting the target entity to receive advertising content; and

sending the advertising content to the target entity.

2. The method of claim 1, wherein each entity operating in the first identifier space is associated with an identifier specific to the first identifier space, and each entity operating in the second identifier space is associated with an identifier specific to the second identifier space.

3. The method of claim 2, wherein the first identifier space is the internet browser identifier space and the second identifier space is the mobile identifier space.

4. The method of claim 2, wherein, for each respective entity of the join panel, an identifier associated with the respective entity of the join panel and specific to the first identifier space is mapped to an identifier associated with the respective entity of the join panel and specific to the second identifier space.

5. The method of claim 1, further comprising identifying the archetypical population in the first identifier space based on pre-defined criteria, wherein each entity in the archetypical population fulfills the pre-defined criteria.

6. The method of claim 1, further comprising analyzing histories of entities in a archetypical population operating in the first identifier space and entities in a standard population operating in the first identifier space to identify the first set of features, wherein the first set of features distinguishes the archetypical population from the standard population.

7. The method of claim 1, wherein identifying the second set of features comprises analyzing histories of the entities of the join panel and entities in a standard population operating in the second identifier space, wherein the second set of features distinguishes entities of the join panel from the standard population.

8. The method of claim 1, wherein identifying the second set of features comprises selecting a plurality of entities from the entities of the join panel based on the scores computed for the entities of the join panel.

9. The method of claim 8, wherein selecting the plurality of entities from the entities of the join panel comprises comparing the scores computed for each entity of the join panel with a pre-determined threshold and selecting entities of the join panel having a score greater than the pre-determined threshold.

10. The method of claim 9, wherein building the second model comprises weighting each feature in the second set of features based on the scores of the plurality of entities selected from the entities of the join panel and having that feature.

11. A non-transitory computer readable storage medium executing computer program instructions for determining the similarity between entities across different identifier spaces, the computer program instructions comprising instructions for:

building a first model specific to a first identifier space based at least on a first set of features associated with an archetypical population operating in the first identifier space, the first set of features determined based on histories associated with the archetypical population in the first identifier space;

identifying a join panel of entities that each operates in both the first identifier space and a second identifier space, each entity of the join panel having a respective first identifier associated with the first identifier space mapped to a respective second identifier associated with the second identifier space;

computing a score for each entity of the join panel by applying the first model to each entity of the join panel, each respective score reflective of the similarity between the respective entity of the join panel and the archetypical population;

identifying a second set of features associated with the entities of the join panel based on the scores, the second set of features determined based on histories associated with the entities of the join panel in the second identifier space;

building a second model specific to the second identifier space based at least on the second set of features;

applying the second model to a target entity operating in the second identifier space to compute a score for the target entity, the score indicating the similarity between the target entity operating in the second identifier space and the archetypical population operating in the first identifier space, wherein an identifier associated with the target entity in the second identifier space is not mapped to an identifier in the first identifier space;

responsive to the score for the target entity indicating the target entity is similar to the archetypical population, targeting the target entity to receive advertising content; and

sending the advertising content to the target entity.

12. The medium of claim 11, wherein each entity operating in the first identifier space is associated with an identifier specific to the first identifier space, and each entity operating in the second identifier space is associated with an identifier specific to the second identifier space.

13. The medium of claim 12, wherein the first identifier space is the internet browser identifier space and the second identifier space is the mobile identifier space.

14. The medium of claim 12, wherein, for each respective entity of the join panel, an identifier associated with the respective entity of the join panel and specific to the first identifier space is mapped to an identifier associated with the respective entity of the join panel and specific to the second identifier space.

15. The medium of claim 11, the instructions further comprising instructions for:

identifying the archetypical population in the first identifier space based on pre-defined criteria, wherein each entity in the archetypical population fulfills the pre-defined criteria.

16. The medium of claim 11, the instructions further comprising instructions for:

analyzing histories of entities in a archetypical population operating in the first identifier space and entities in a standard population operating in the first identifier space to identify the first set of features, wherein the first set of features distinguishes the archetypical population from the standard population.

17. The medium of claim 11, wherein identifying the second set of features comprises analyzing histories of the entities of the join panel and entities in a standard population operating in the second identifier space, wherein the second set of features distinguishes entities of the join panel from the standard population.

18. The medium of claim 11, wherein identifying the second set of features comprises selecting a plurality of entities from the entities of the join panel based on the scores computed for the entities of the join panel.

19. The medium of claim 18, wherein selecting the plurality of entities from the entities of the join panel comprises comparing the scores computed for each entity of the join panel with a pre-determined threshold and selecting entities of the join panel having a score greater than the pre-determined threshold.

20. The medium of claim 19, wherein building the second model comprises weighting each feature in the second set of features based on the scores of the plurality of entities selected from the entities of the join panel and having that feature.

21. A system comprising:

a processor;

a computer readable storage medium storing processor-executable computer program instructions for determining the similarity between entities across different identifier spaces, the computer program instructions comprising instructions for;

building a first model specific to a first identifier space based at least on a first set of features associated with an archetypical population operating in the first identifier space, the first set of features determined based on histories associated with the archetypical population in the first identifier space;

identifying a join panel of entities that each operates in both the first identifier space and a second identifier space, each entity of the join panel having a respective first identifier associated with the first identifier space mapped to a respective second identifier associated with the second identifier space;

computing a score for each entity of the join panel by applying the first model to each entity of the join panel, each respective score reflective of the similarity between the respective entity of the join panel and the archetypical population;

identifying a second set of features associated with the entities of the join panel based on the scores, the second set of features determined based on histories associated with the entities of the join panel in the second identifier space;

building a second model specific to the second identifier space based at least on the second set of features;

applying the second model to a target entity operating in the second identifier space to compute a score for the target entity, the score indicating the similarity between the target entity operating in the second identifier space and the archetypical population operating in the first identifier space, wherein an identifier associated with the target entity in the second identifier space is not mapped to an identifier in the first identifier space;

responsive to the score for the target entity indicating the target entity is similar to the archetypical population, targeting the target entity to receive advertising content; and

sending the advertising content to the target entity.