Selecting web site content to be displayed to a web site visitor based upon a probability distribution

• US 20020062247A1
• Filed: 06/04/2001
• Published: 05/23/2002
• Est. Priority Date: 08/29/2000
• Status: Abandoned Application

• Current Assignee: Valueclick
• Original Assignee: Valueclick
• Inventors: Allen, Bradley P.
• Application Number:
  US09/874,948
• Publication Number:
  US 20020062247A1
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

One embodiment of the present invention provides a system dynamically selects a content option from a plurality of content options in order to display the content option to a user who is browsing through a web site. The system operates by receiving a request for content from a web browser that is being operated by the user. In response to this request, the system calculates a probability distribution across the plurality of content options that can be sent to the web browser, and then selects the content option at random from the plurality of content options based upon the calculated probability distribution. Next, the system sends the selected content option to the web browser and then allows the web browser to display the selected content option to the user of the web browser. The system then receives a response to the selected content option from the user of the web browser, and uses this response to update a future probability distribution across the plurality of content options.

Claims

1. A method for dynamically selecting a content option from a plurality of content options in order to display the content option to a user who is browsing through a web site, comprising:

receiving a request for content from a web browser that is being operated by the user;

calculating a probability distribution across the plurality of content options that can be sent to the web browser;

selecting the content option at random from the plurality of content options based upon the calculated probability distribution;

sending the selected content option to the web browser;

allowing the web browser to display the selected content option to the user of the web browser;

receiving a response to the selected content option from the user of the web browser; and

using the response to update a future probability distribution across the plurality of content options that is used in making a future selection of a content option.

2. The method of claim 1, wherein using the response to update the future probability distribution across the plurality of content options includes using a multiplicative update function to update the future probability distribution.

3. The method of claim 1, wherein calculating the probability distribution across the plurality of content options includes calculating the probability distribution by substantially optimizing an expected payoff of a selection by the user in response to the selected content option.

4. The method of claim 1, wherein calculating the probability distribution across the plurality of content options includes calculating the probability distribution based upon a customer profile related to the user.

5. The method of claim 1, wherein calculating the probability distribution across the plurality of content options includes calculating the probability distribution based upon an interaction context related to the web site.

6. The method of claim 1, wherein calculating the probability distribution across the plurality of content options includes combining probability distributions from a plurality of automated experts that provide different probability distributions.

7. The method of claim 6, wherein updating the future probability distribution involves updating a plurality of weights associated with the plurality of automated experts.

8. The method of claim 1, wherein calculating the probability distribution includes providing a non-zero probability for every content option in the plurality of content options so that feedback will eventually be generated for every content option.

9. The method of claim 1, wherein the plurality of content options include purchase options, links to other web locations, links to other web pages, and promotional material for products.

10. The method of claim 1, wherein calculating the probability distribution and selecting the content option involve executing program code that is structured to play a repeated two-player game in which the user of the web browser is an adversary that responds to selected content options.

11. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for dynamically selecting a content option from a plurality of content options in order to display the content option to a user who is browsing through a web site, the method comprising:

receiving a request for content from a web browser that is being operated by the user;

calculating a probability distribution across the plurality of content options that can be sent to the web browser;

selecting the content option at random from the plurality of content options based upon the calculated probability distribution;

sending the selected content option to the web browser;

allowing the web browser to display the selected content option to the user of the web browser;

receiving a response to the selected content option from the user of the web browser; and

using the response to update a future probability distribution across the plurality of content options that is used in making a future selection of a content option.

12. The computer-readable storage medium of claim 11, wherein using the response to update the future probability distribution across the plurality of content options includes using a multiplicative update function to update the future probability distribution.

13. The computer-readable storage medium of claim 11, wherein calculating the probability distribution across the plurality of content options includes calculating the probability distribution by substantially optimizing an expected payoff of a selection by the user in response to the selected content option.

14. The computer-readable storage medium of claim 11, wherein calculating the probability distribution across the plurality of content options includes calculating the probability distribution based upon a customer profile related to the user.

15. The computer-readable storage medium of claim 11, wherein calculating the probability distribution across the plurality of content options includes calculating the probability distribution based upon an interaction context related to the web site.

16. The computer-readable storage medium of claim 11, wherein calculating the probability distribution across the plurality of content options includes combining probability distributions from a plurality of automated experts that provide different probability distributions.

17. The computer-readable storage medium of claim 16, wherein updating the future probability distribution involves updating a plurality of weights associated with the plurality of automated experts.

18. The computer-readable storage medium of claim 11, wherein calculating the probability distribution includes providing a non-zero probability for every content option in the plurality of content options so that feedback will eventually be generated for every content option.

19. The computer-readable storage medium of claim 11, wherein the plurality of content options include purchase options, links to other web locations, links to other web pages, and promotional material for products.

20. The computer-readable storage medium of claim 11, wherein calculating the probability distribution and selecting the content option involve executing program code that is structured to play a repeated two-player game in which the user of the web browser is an adversary that responds to selected content options.

21. An apparatus that dynamically selects a content option from a plurality of content options in order to display the content option to a user who is browsing through a web site, comprising:

a receiving mechanism that is configured to receive a request for content from a web browser that is being operated by the user;

a calculating mechanism that is configured to calculate a probability distribution across the plurality of content options that can be sent to the web browser;

a selection mechanism that is configured to select the content option at random from the plurality of content options based upon the calculated probability distribution;

a sending mechanism that is configured to send the selected content option to the web browser;

wherein the receiving mechanism is additionally configured to receive a response to the selected content option from the user of the web browser; and

an updating mechanism that is configured to use the response to update a future probability distribution across the plurality of content options that is used in making a future selection of a content option.

22. The apparatus of claim 21, wherein the updating mechanism is configured to use a multiplicative update function to update the future probability distribution.

23. The apparatus of claim 21, wherein the calculating mechanism is configured to calculate the probability distribution by substantially optimizing an expected payoff of a selection by the user in response to the selected content option.

24. The apparatus of claim 21, wherein the calculating mechanism is configured to calculate the probability distribution based upon a customer profile related to the user.

25. The apparatus of claim 21, wherein the calculating mechanism is configured to calculate the probability distribution based upon an interaction context related to the web site.

26. The apparatus of claim 21, wherein the calculating mechanism is configured to combine probability distributions from a plurality of automated experts that provide different probability distributions.

27. The apparatus of claim 26, wherein the updating mechanism is configured to update a plurality of weights associated with the plurality of automated experts.

28. The apparatus of claim 21, wherein the calculating mechanism is configured to provide a non-zero probability for every content option in the plurality of content options so that feedback will eventually be generated for every content option.

29. The apparatus of claim 21, wherein the plurality of content options include purchase options, links to other web locations, links to other web pages, and promotional material for products.

30. The apparatus of claim 21, wherein the calculating mechanism and the selection mechanism a re configured to play a repeated two-player game in which the user of the web browser is an adversary that responds to selected content options.