METHODS, APPARATUSES, AND COMPUTER PROGRAM PRODUCTS FOR PROVIDING TARGETED ADVERTISING

• US 20100169157A1
• Filed: 12/30/2008
• Published: 07/01/2010
• Est. Priority Date: 12/30/2008
• Status: Abandoned Application

• Current Assignee: Nokia Corporation
• Original Assignee: Nokia Corporation
• Inventors: Takala, Jari, Muhonen, Ahti
• Application Number:
  US12/346,424
• Publication Number:
  US 20100169157A1
• Time in Patent Office: Days
• US Class Current: 705/10
• 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 30/0244 Optimization
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

A method, apparatus, and computer program product are provided for providing targeted advertising. An apparatus may include a processor configured to generate a targeting vector and provide the targeting vector to an advertising platform to permit one or more advertisements to be selected based at least in part upon the provided targeting vector. The processor may be further configured to receive the one or more advertisements from the advertising platform. The processor may additionally be configured to monitor a user'"'"'s interaction with the received advertisements to determine an effectiveness level of the advertisements. The processor may be further configured to update the targeting vector when the determined effectiveness level is less than a target effectiveness level by modifying the targeting vector to enhance the effectiveness level of advertisements selected based at least in part upon the targeting vector. Corresponding methods and computer program products are also provided.

Claims

1. An apparatus comprising a processor configured to:

determine one or more user parameters, each user parameter defining an attribute of a user of the apparatus, wherein the processor is configured to determine each user parameter based at least in part upon locally collected user data;

generate a targeting vector based at least in part upon the one or more determined user parameters;

provide the targeting vector to an advertising platform to permit one or more advertisements to be selected based at least in part upon the provided targeting vector;

receive the one or more advertisements from the advertising platform;

present the one or more received advertisements to the user;

monitor the user'"'"'s interaction with the received one or more advertisements to determine an effectiveness level of the one or more received advertisements; and

update the targeting vector when the determined effectiveness level is less than a target effectiveness level by modifying the targeting vector to enhance the effectiveness level of advertisements selected based at least in part upon the targeting vector, the processor configured to provide the updated targeting vector to the advertising platform to permit future received advertisements to be selected based at least in part upon the updated targeting vector.

2. An apparatus according to claim 1, wherein the processor is configured to implement a feedback loop such that the targeting vector is periodically updated when the effectiveness level of the received one or more advertisements is less than the target effectiveness level by modifying the targeting vector to enhance the effectiveness level of advertisements selected based at least in part upon the targeting vector and to provide each updated targeting vector to the advertising platform such that future received advertisements are selected based at least in part upon the most recent updated targeting vector.

3. An apparatus according to claim 1, wherein the processor is further configured to receive a first intermediate targeting vector describing one or more attributes of the user from a remote computing device;

and wherein the processor is configured to generate the targeting vector by;

generating a second intermediate targeting vector based at least in part upon the one or more determined user parameters; and

combining the first and second intermediate targeting vectors to generate the targeting vector.

4. An apparatus according to claim 3, wherein the processor is configured to:

combine the first and second intermediate targeting vectors by applying a first weight to the first intermediate targeting vector and a second weight to the second intermediate targeting vector and summing the weighted first and second intermediate targeting vectors; and

wherein the processor is configured to update the targeting vector by adjusting at least one of the first weight applied to the first intermediate targeting vector or the second weight applied to the second intermediate targeting vector and re-summing the weighted first and second intermediate targeting vectors.

5. An apparatus according to claim 1, wherein the processor is further configured to:

receive, from the advertising platform, one or more advertisement target vectors, wherein each advertisement target vector corresponds with at least one received advertisement; and

monitor the user'"'"'s interaction with the received one or more advertisements to determine advertisements with which the user has positively interacted; and

wherein the processor is configured to update the targeting vector when the determined effectiveness level is less than the target effectiveness level by biasing the targeting vector toward an area where one or more advertisement targeting vectors corresponding to the determined advertisements are located.

6. An apparatus according to claim 1, wherein the processor is further configured to:

collect usage data related to the user'"'"'s usage of the apparatus;

aggregate the collected usage data into one or more categories of collected data; and

analyze each category of collected data to determine a user profile data point for each category of data; and

wherein the processor is configured to determine each user parameter based at least in part upon one or more determined user profile data points.

7. An apparatus according to claim 1, wherein the processor is configured to:

monitor the user'"'"'s interactions by monitoring whether the user clicks through any of the advertisements to determine the user'"'"'s click through rate; and

update the targeting vector when the determined click through rate is less than a target click through rate.

8. An apparatus according to claim 1, further comprising a display in communication with the processor;

wherein the processor is further configured to present the one or more received advertisements to the user by rendering the received one or more advertisements on the display.

9. A computer program product comprising at least one computer-readable storage medium having computer-readable program instructions stored therein, the computer-readable program instructions comprising:

a program instruction for determining one or more user parameters, wherein each user parameter defines an attribute of a user and is determined based at least in part upon locally collected user data;

a program instruction for generating a targeting vector based at least in part upon the one or more determined user parameters;

a program instruction for providing the targeting vector to an advertising platform to permit one or more advertisements to be selected based at least in part upon the provided targeting vector;

a program instruction for receiving the one or more advertisements from the advertising platform;

a program instruction for presenting the one or more received advertisements to the user;

a program instruction for monitoring the user'"'"'s interaction with the received one or more advertisements to determine an effectiveness level of the one or more received advertisements;

a program instruction for updating the targeting vector when the determined effectiveness level is less than a target effectiveness level by modifying the targeting vector to enhance the effectiveness level of advertisements selected based at least in part upon the targeting vector; and

a program instruction for providing the updated targeting vector to the advertising platform to permit future received advertisements to be selected based at least in part upon the updated targeting vector.

10. A computer program product according to claim 9, further comprising:

a program instruction for implementing a feedback loop such that the targeting vector is periodically updated when the effectiveness level of the received one or more advertisements is less than the target effectiveness level by modifying the targeting vector to enhance the effectiveness level of advertisements selected based at least in part upon the targeting vector and for providing each updated targeting vector to the advertising platform such that future received advertisements are selected based at least in part upon the most recent updated targeting vector.

11. A computer program product according to claim 9, further comprising a program instruction for receiving a first intermediate targeting vector describing one or more attributes of the user from a remote computing device;

and wherein the program instruction for generating a targeting vector comprises instructions for generating the targeting vector by;

generating a second intermediate targeting vector based at least in part upon the one or more determined user parameters; and

combining the first and second intermediate targeting vectors to generate the targeting vector.

12. A computer program product according to claim 11, wherein the program instruction for generating a targeting vector comprises instructions for combining the first and second intermediate targeting vectors by applying a first weight to the first intermediate targeting vector and a second weight to the second intermediate targeting vector and summing the weighted first and second intermediate targeting vectors;

and

wherein the program instruction for updating the targeting vector comprises instructions for updating the targeting vector by adjusting at least one of the first weight applied to the first intermediate targeting vector or the second weight applied to the second intermediate targeting vector and re-summing the weighted first and second intermediate targeting vectors.

13. A computer program product according to claim 9, further comprising:

a program instruction for receiving, from the advertising platform, one or more advertisement target vectors, wherein each advertisement target vector corresponds with at least one received advertisement; and

a program instruction for monitoring the user'"'"'s interaction with the received one or more advertisements to determine advertisements with which the user has positively interacted; and

wherein the program instruction for updating the targeting vector includes instructions for updating the targeting vector when the determined effectiveness level is less than the target effectiveness level by biasing the targeting vector toward an area where one or more advertisement targeting vectors corresponding to the determined advertisements are located.

14. A computer program product according to claim 9, further comprising:

a program instruction for collecting usage data related to the user'"'"'s usage of a computing device;

a program instruction for aggregating the collected usage data into one or more categories of collected data; and

a program instruction for analyzing each category of collected data to determine a user profile data point for each category of data; and

wherein the program instruction for determining one or more user parameters includes instructions for determining each user parameter based at least in part upon one or more determined user profile data points.

15. A method comprising:

determining one or more user parameters, wherein each user parameter defines an attribute of a user of a computing device and is determined based at least in part upon locally collected user data;

generating, with a processor embodied in the computing device, a targeting vector based at least in part upon the one or more determined user parameters;

providing the targeting vector to an advertising platform to permit one or more advertisements to be selected based at least in part upon the provided targeting vector;

receiving the one or more advertisements from the advertising platform;

presenting the one or more received advertisements to the user;

monitoring the user'"'"'s interaction with the received one or more advertisements to determine an effectiveness level of the one or more received advertisements;

updating the targeting vector when the determined effectiveness level is less than a target effectiveness level by modifying the targeting vector to enhance the effectiveness level of advertisements selected based at least in part upon the targeting vector; and

providing the updated targeting vector to the advertising platform to permit future received advertisements to be selected based at least in part upon the updated targeting vector.

16. A method according to claim 15, further comprising:

implementing a feedback loop such that the targeting vector is periodically updated when the effectiveness level of the received one or more advertisements is less than the target effectiveness level by modifying the targeting vector to enhance the effectiveness level of advertisements selected based at least in part upon the targeting vector and providing each updated targeting vector to the advertising platform such that future received advertisements are selected based at least in part upon the most recent updated targeting vector.

17. A method according to claim 15, further comprising receiving a first intermediate targeting vector describing one or more attributes of the user from a remote computing device;

and wherein generating a targeting vector comprises generating the targeting vector by;

generating a second intermediate targeting vector based at least in part upon the one or more determined user parameters; and

combining the first and second intermediate targeting vectors to generate the targeting vector.

18. A method according to claim 17, wherein combining the first and second intermediate targeting vectors comprises combining the first and second intermediate targeting vectors by applying a first weight to the first intermediate targeting vector and a second weight to the second intermediate targeting vector and summing the weighted first and second intermediate targeting vectors;

and

wherein updating the targeting vector comprises updating the targeting vector by adjusting at least one of the first weight applied to the first intermediate targeting vector or the second weight applied to the second intermediate targeting vector and re-summing the weighted first and second intermediate targeting vectors.

19. A method according to claim 15, further comprising:

receiving, from the advertising platform, one or more advertisement target vectors, wherein each advertisement target vector corresponds with at least one received advertisement; and

monitoring the user'"'"'s interaction with the received one or more advertisements to determine advertisements with which the user has positively interacted; and

wherein updating the targeting vector comprises updating the targeting vector when the determined effectiveness level is less than the target effectiveness level by biasing the targeting vector toward an area where one or more advertisement targeting vectors corresponding to the determined advertisements are located.

20. A method according to claim 15, further comprising:

collecting usage data related to the user'"'"'s usage of the computing device;

aggregating the collected usage data into one or more categories of collected data; and

analyzing each category of collected data to determine a user profile data point for each category of data; and

wherein determining one or more user parameters comprises determining each user parameter based at least in part upon one or more determined user profile data points.