PRIVACY-SENSITIVE METHODS, SYSTEMS, AND MEDIA FOR TARGETING ONLINE ADVERTISEMENTS USING BRAND AFFINITY MODELING

• US 20100205057A1
• Filed: 02/05/2010
• Published: 08/12/2010
• Est. Priority Date: 02/06/2009
• Status: Abandoned Application

• Current Assignee: Dstillery, Inc.
• Original Assignee: Dstillery, Inc.
• Inventors: Dalessandro, Brian, May, Brian, Provost, Foster John, Hook, Rodney
• Application Number:
  US12/700,728
• Publication Number:
  US 20100205057A1
• Time in Patent Office: Days
• US Class Current: 705/14.520
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0254 based on statistics

Abstract

Privacy-sensitive methods, systems, and media for targeting online advertisements using brand affinity modeling are provided. In accordance with some embodiments, a method for constructing brand audiences for targeting advertisements is provided, the method comprising: collecting visitation data relating to user-generated micro-content from a plurality of browsers; extracting a quasi-social network from the collected visitation data, wherein the quasi-social network comprises a plurality of links that are induced between the plurality of browsers visiting the user-generated micro-content; selecting seed nodes from the plurality of browsers, wherein the selected seed nodes have performed a brand action relating to the user-generated micro-content that is indicative of brand affinity; determining candidate nodes from the plurality of browsers based at least in part on a distance from the seed nodes in the quasi-social network; calculating a brand proximity score for each of the candidate nodes, wherein the brand proximity score includes one or more brand proximity measures and wherein the brand proximity score is an aggregated distance measurement between the candidate nodes and the seed nodes; generating a ranking of the candidate nodes based on the brand proximity score; and selecting a brand audience for serving an advertisement based on the generated ranking.

Claims

1. A method for constructing brand audiences for targeting advertisements, the method comprising:

collecting visitation data relating to user-generated micro-content from a plurality of browsers;

extracting a quasi-social network from the collected visitation data, wherein the quasi-social network comprises a plurality of links that are induced between the plurality of browsers visiting the user-generated micro-content;

selecting seed nodes from the plurality of browsers, wherein the selected seed nodes have performed a brand action relating to the user-generated micro-content that is indicative of brand affinity;

determining candidate nodes from the plurality of browsers based at least in part on a distance from the seed nodes in the quasi-social network;

calculating a brand proximity score for each of the candidate nodes, wherein the brand proximity score includes one or more brand proximity measures and wherein the brand proximity score is an aggregated distance measurement between the candidate nodes and the seed nodes;

generating a ranking of the candidate nodes based on the brand proximity score; and

selecting a brand audience for serving an advertisement based on the generated ranking.

2. The method of claim 1, further comprising associating weights with each of the plurality of link in the quasi-social network, wherein the weights indicate whether one of the browsers has visited a particular piece of user-generated micro-content.

3. The method of claim 1, further comprising generating a bipartite content affinity network graph that maps the candidate nodes and the seed nodes to user-generated micro-content.

4. The method of claim 1, wherein one of the one or more brand proximity measures calculates the number of unique user-generated content pages that link one of the nodes with one or more of the seed nodes.

5. The method of claim 1, wherein one of the one or more brand proximity measures calculates the maximum number of unique user-generated content pages that link one of the nodes with one or more of the seed nodes.

6. The method of claim 1, wherein one of the one or more brand proximity measures calculates the minimum Euclidian distance between a normalized content vector of one of the candidate nodes and the normalized content vector of any of the seed nodes.

7. The method of claim 1, wherein one of the one or more brand proximity measures calculates the maximum cosine similarity of a content vector of one of the candidate nodes and the content vector of any of the seed nodes.

8. The method of claim 1, wherein one of the one or more brand proximity measures calculates the ratio of the number of seed nodes to the number of candidate nodes.

9. The method of claim 1, wherein one of the candidate nodes generates a page of user-generated micro-content and wherein one of the one or more brand proximity measures determines whether one or more of the seed nodes has visited the page of user-generated content generated by that candidate node.

10. The method of claim 1, wherein the one or more brand proximity measures are calculated over a collection of user-generated content pages and wherein the collection of user-generated content pages comprises at least one of:

all user-generated content, micro-user-generated content, and macro-user-generated content.

11. The method of claim 1, further comprising predicting conversion of the advertisements by:

serving an advertisement to nodes in the brand audience;

generating a prediction model for each candidate node;

inserting an additional variable that indicate whether each candidate node performed one or more brand actions; and

training the prediction model to estimate the likelihood of brand action by future candidate nodes.

12. The method of claim 1, further comprising evaluating the selected brand audience by comparing the density of browsers within the selected brand audience that have performed the brand action with the density of browsers within all nodes that have performed the brand action.

13. A system for generating brand audiences for targeting advertisements, the system comprising:

a processor that;

collects visitation data relating to user-generated micro-content from a plurality of browsers;

extracts a quasi-social network from the collected visitation data, wherein the quasi-social network comprises a plurality of links that are induced between the plurality of browsers visiting the user-generated micro-content;

selects seed nodes from the plurality of browsers, wherein the selected seed nodes have performed a brand action relating to the user-generated micro-content that is indicative of brand affinity;

determines candidate nodes from the plurality of browsers based at least in part on a distance from the seed nodes in the quasi-social network;

calculates a brand proximity score for each of the candidate nodes, wherein the brand proximity score includes one or more brand proximity measures and wherein the brand proximity score is an aggregated distance measurement between the candidate nodes and the seed nodes;

generates a ranking of the candidate nodes based on the brand proximity score; and

selects a brand audience for serving an advertisement based on the generated ranking.

14. The system of claim 13, wherein the processor is further configured to associate weights with each of the plurality of link in the quasi-social network, wherein the weights indicate whether one of the browsers has visited a particular piece of user-generated micro-content.

15. The system of claim 13, wherein the processor is further configured to generate a bipartite content affinity network graph that maps the candidate nodes and the seed nodes to user-generated micro-content.

16. The system of claim 13, wherein the processor is further configured to calculate the number of unique user-generated content pages that link one of the nodes with one or more of the seed nodes.

17. The system of claim 13, wherein the processor is further configured to calculate the maximum number of unique user-generated content pages that link one of the nodes with one or more of the seed nodes.

18. The system of claim 13, wherein the processor is further configured to calculate the minimum Euclidian distance between a normalized content vector of one of the candidate nodes and the normalized content vector of any of the seed nodes.

19. The system of claim 13, wherein the processor is further configured to calculate the maximum cosine similarity of a content vector of one of the candidate nodes and the content vector of any of the seed nodes.

20. The system of claim 13, wherein the processor is further configured to calculate the ratio of the number of seed nodes to the number of candidate nodes.

21. The system of claim 13, wherein one of the candidate nodes generates a page of user-generated micro-content and wherein the processor is further configured to determine whether one or more of the seed nodes has visited the page of user-generated content generated by that candidate node.

22. The system of claim 13, wherein the processor is further configured to calculate the one or more brand proximity measures over a collection of user-generated content pages and wherein the collection of user-generated content pages comprises at least one of:

all user-generated content, micro-user-generated content, and macro-user-generated content.

23. The system of claim 13, wherein the processor is further configured to predict conversion of the advertisements by:

serving an advertisement to nodes in the brand audience;

generating a prediction model for each candidate node;

inserting an additional variable that indicate whether each candidate node performed one or more brand actions; and

training the prediction model to estimate the likelihood of brand action by future candidate nodes.

24. The system of claim 13, wherein the processor is further configured to evaluate the selected brand audience by comparing the density of browsers within the selected brand audience that have performed the brand action with the density of browsers within all nodes that have performed the brand action.

25. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for constructing brand audiences for targeting advertisements, the method comprising:

collecting visitation data relating to user-generated micro-content from a plurality of browsers;

extracting a quasi-social network from the collected visitation data, wherein the quasi-social network comprises a plurality of links that are induced between the plurality of browsers visiting the user-generated micro-content;

selecting seed nodes from the plurality of browsers, wherein the selected seed nodes have performed a brand action relating to the user-generated micro-content that is indicative of brand affinity;

determining candidate nodes from the plurality of browsers based at least in part on a distance from the seed nodes in the quasi-social network;

calculating a brand proximity score for each of the candidate nodes, wherein the brand proximity score includes one or more brand proximity measures and wherein the brand proximity score is an aggregated distance measurement between the candidate nodes and the seed nodes;

generating a ranking of the candidate nodes based on the brand proximity score; and

selecting a brand audience for serving an advertisement based on the generated ranking.

26. The non-transitory computer-readable medium of claim 25, wherein the method further comprises associating weights with each of the plurality of link in the quasi-social network, wherein the weights indicate whether one of the browsers has visited a particular piece of user-generated micro-content.

27. The non-transitory computer-readable medium of claim 25, wherein the method further comprises generating a bipartite content affinity network graph that maps the candidate nodes and the seed nodes to user-generated micro-content.

28. The non-transitory computer-readable medium of claim 25, wherein one of the one or more brand proximity measures calculates the number of unique user-generated content pages that link one of the nodes with one or more of the seed nodes.

29. The non-transitory computer-readable medium of claim 25, wherein one of the one or more brand proximity measures calculates the maximum number of unique user-generated content pages that link one of the nodes with one or more of the seed nodes.

30. The non-transitory computer-readable medium of claim 25, wherein one of the one or more brand proximity measures calculates the minimum Euclidian distance between a normalized content vector of one of the candidate nodes and the normalized content vector of any of the seed nodes.

31. The non-transitory computer-readable medium of claim 25, wherein one of the one or more brand proximity measures calculates the maximum cosine similarity of a content vector of one of the candidate nodes and the content vector of any of the seed nodes.

32. The non-transitory computer-readable medium of claim 25, wherein one of the one or more brand proximity measures calculates the ratio of the number of seed nodes to the number of candidate nodes.

33. The non-transitory computer-readable medium of claim 25, wherein one of the candidate nodes generates a page of user-generated micro-content and wherein one of the one or more brand proximity measures determines whether one or more of the seed nodes has visited the page of user-generated content generated by that candidate node.

34. The non-transitory computer-readable medium of claim 25, wherein the one or more brand proximity measures are calculated over a collection of user-generated content pages and wherein the collection of user-generated content pages comprises at least one of all user-generated content, micro-user-generated content, and macro-user-generated content.

35. The non-transitory computer-readable medium of claim 25, wherein the method further comprises predicting conversion of the advertisements by:

serving an advertisement to nodes in the brand audience;

generating a prediction model for each candidate node;

inserting an additional variable that indicate whether each candidate node performed one or more brand actions; and

training the prediction model to estimate the likelihood of brand action by future candidate nodes.

36. The non-transitory computer-readable medium of claim 25, wherein the method further comprises evaluating the selected brand audience by comparing the density of browsers within the selected brand audience that have performed the brand action with the density of browsers within all nodes that have performed the brand action.