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

• US 10,248,961 B1
• Filed: 03/23/2016
• Issued: 04/02/2019
• 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:
  US15/078,160
• Time in Patent Office: 1,105 Days
• Field of Search: None
• 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 a similarity between entities across different identifier spaces, the method comprising:

building a first model specific to a first identifier space using a first set of features correlated with an archetypical population having made a product purchase in the first identifier space as opposed to a standard population, both the archetypical population and the standard population operating in the first identifier space, the first set of features 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 of an Internet browser of the first identifier space mapped to a respective second identifier of a mobile application of the second identifier space;

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

selecting a set of contributing entities comprising a plurality of entities from the join panel, each of the contributing entities having a respective score above a threshold;

building a second model specific to the mobile application of the second identifier space by selecting a second set of features correlated with the set of contributing entities as opposed to a second standard population, both the contributing entities and the second standard population operating in the second identifier space, the second set of features associated with the contributing entities in the second identifier space;

predicting the similarity between a target entity operating in the second identifier space and the archetypical population operating in the first identifier space by applying the second model to the target entity operating in the second 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 predicted similarity indicating the target entity is likely to be similar to the archetypical population, targeting the target entity to receive advertising content related to the product; and

sending the advertising content to the mobile application of 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 1 wherein:

each entity in the join panel is associated with a respective identifier specific to the first identifier space and a respective identifier specific to the second identifier space, the respective identifier specific to the first identifier space mapped to the respective identifier specific to the second identifier space.

4. The method of claim 1 wherein:

the target entity operating in the second identifier space also operates in the first identifier space, and an identifier associated with the target entity and specific to the first identifier space is not mapped to an identifier associated with the target entity and specific to the second identifier space.

5. The method of claim 1 further comprising:

selecting the archetypical population in the first identifier space according to pre-defined criteria, wherein each entity in the archetypical population fulfills the pre-defined criteria.

6. The method of claim 1 further comprising:

selecting the archetypical population by analyzing histories of entities operating in the first identifier space.

7. The method of claim 1 wherein:

the second set of features are found in the histories of the contributing entities associated with the second identifier space.

8. The method of claim 1 wherein:

building the second model comprises weighting each feature in the second set of features according to the scores of the contributing entities having that feature.

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

building a first model specific to a first identifier space using a first set of features correlated with an archetypical population having made a product purchase in the first identifier space as opposed to a standard population, both the archetypical population and the standard population operating in the first identifier space, the first set of features 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 of an Internet browser of the first identifier space mapped to a respective second identifier of a mobile application of the second identifier space;

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

selecting a set of contributing entities comprising a plurality of entities from the join panel, each of the contributing entities having a respective score above a threshold;

building a second model specific to the mobile application of the second identifier space by selecting a second set of features correlated with the set of contributing entities as opposed to a second standard population, both the contributing entities and the second standard population operating in the second identifier space, the second set of features associated with the contributing entities in the second identifier space;

predicting the similarity between a target entity operating in the second identifier space and the archetypical population operating in the first identifier space by applying the second model to the target entity operating in the second 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 predicted similarity indicating the target entity is likely to be similar to the archetypical population, targeting the target entity to receive advertising content related to the product; and

sending the advertising content to the mobile application of the target entity.

10. The medium of claim 9 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.

11. The medium of claim 9 wherein:

each entity in the join panel is associated with a respective identifier specific to the first identifier space and a respective identifier specific to the second identifier space, the respective identifier specific to the first identifier space mapped to the respective identifier specific to the second identifier space.

12. The medium of claim 9 wherein:

the target entity operating in the second identifier space also operates in the first identifier space, and an identifier associated with the target entity and specific to the first identifier space is not mapped to an identifier associated with the target entity and specific to the second identifier space.

13. The medium of claim 9 wherein the instructions further comprise:

selecting the archetypical population in the first identifier space according to pre-defined criteria, wherein each entity in the archetypical population fulfills the pre-defined criteria.

14. The medium of claim 9 wherein the instructions further comprise:

selecting the archetypical population by analyzing histories of entities operating in the first identifier space.

15. The medium of claim 9 wherein the instructions further comprise:

building the second model by weighting each feature in the second set of features according to the scores of the contributing entities having that feature.

16. A system comprising:

a processor;

a computer readable storage medium storing processor-executable computer program instructions for predicting 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 using a first set of features correlated with an archetypical population having made a product purchase in the first identifier space as opposed to a standard population, both the archetypical population and the standard population operating in the first identifier space, the first set of features 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 of an Internet browser of the first identifier space mapped to a respective second identifier of a mobile application of the second identifier space;

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

selecting a set of contributing entities comprising a plurality of entities from the join panel, each of the contributing entities having a respective score above a threshold;

building a second model specific to the mobile application of the second identifier space by selecting a second set of features correlated with the set of contributing entities as opposed to a second standard population, both the contributing entities and the second standard population operating in the second identifier space, the second set of features associated with the contributing entities in the second identifier space;

predicting the similarity between a target entity operating in the second identifier space and the archetypical population operating in the first identifier space by applying the second model to the target entity operating in the second 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 predicted similarity indicating the target entity is likely to be similar to the archetypical population, targeting the target entity to receive advertising content related to the product; and

sending the advertising content to the mobile application of the target entity.