Optimizing Advertisement Selection in Contextual Advertising Systems

• US 20110196733A1
• Filed: 02/05/2010
• Published: 08/11/2011
• Est. Priority Date: 02/05/2010
• Status: Abandoned Application

• Current Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Original Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Inventors: Mao, Jianchang (JC), Zhang, Ruofei (Bruce), Li, Wei, Cui, Ying
• Application Number:
  US12/701,031
• Publication Number:
  US 20110196733A1
• Time in Patent Office: Days
• US Class Current: 705/14.42
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0243 Comparative campaigns

Abstract

A contextual advertising system optimizes computer selection of low performance ranked messages and high performance ranked messages for display on a network location. The system divides a ranked group of online messages into a first list, a second list, and a promotion set. Each message in the first list has a performance score that is greater than each performance score of messages in the second list and the promotion set. The system moves a message within the promotion set to a third list as a function of a confidence value and moves a message from one of the third list and the second list to the first list based on an experiment event outcome. The system transmits top messages in the first list over a network for display at a recipient computer.

Claims

1. A computer-implemented method to optimize selection of low performance ranked messages and high performance ranked messages for display on a network location, the method comprising:

presenting, at a computer, a group of online messages ranked according to performance scores;

processing, in the computer, the ranked group of online messages by;

dividing the ranked group of online messages into a first list, a second list, and a promotion set, wherein each message in the first list has a performance score that is greater than each performance score of messages in the second list and the promotion set;

moving a message within the promotion set to a third list as a function of a confidence value;

moving a message from one of the third list and the second list to the first list based on an experiment event outcome; and

transmitting over a network, from a computer, messages in the first list for display at a recipient computer.

2. The method of claim 1, where dividing the ranked group of online messages into the promotion set includes moving into the promotion set only those messages having a quantity of impressions that is less than a predetermined threshold number of impressions.

3. The method of claim 1, where the confidence value for a first message is a function of a quantity of impressions for that message.

4. The method of claim 3, where the confidence value C₁ for the first message is determined according to the equation

C₁=tan h(imp/b)

where

imp is an average number of impressions,

b is a predetermined number of aggregations in that a b parameter may be a parameter that controls how much historical data may be needed to trust an nCTR score,

tan h is a hyperbolic tangent function, and

C₁ is a message confidence value for a given message.

5. The method of claim 1, where the confidence value for a first message is a function of a statistical distribution of a click-through rate for that message.

6. The method of claim 5, where the confidence value C₂ for the first message is determined according to the equation

C₂=Variance(P(θ

|α

, β

, n, k))

where

α

, β

are two positive shape parameters that parameterize a continuous probability distribution defined on an interval [0, 1],

n is a number of observed impressions made by a message,

k is a number of observed click-throughs received by a message (both n and k refer to a page message pair),

θ

is a click-through rate (CTR) of a given page-message pair and is a random variable that corresponds to an underlying CTR for a page message pair,

P(θ

|, α

, β

, n, k) is a posterior distribution of a CTR given a prior Beta (alpha, beta) and an observations n and k,

Variance of a random variable or distribution (P(θ

|α

, β

, n, k)) is an expected square deviation of that variable from its expected value or mean as a measure of an amount of variation of all scores for a variable (not just extremes which give the range), and

C₂ is a message confidence value for a given message.

7. A computer readable medium containing executable instructions stored thereon, which, when executed in a computer, cause the computer to optimize selection of low performance ranked messages and high performance ranked messages for display on a network location, the instructions for:

presenting, at a computer, a group of online messages ranked according to performance scores;

processing, in the computer, the ranked group of online messages by;

dividing the ranked group of online messages into a first list, a second list, and a promotion set, wherein each message in the first list has a performance score that is greater than each performance score of messages in the second list and the promotion set;

moving a message within the promotion set to a third list as a function of a confidence value;

moving a message from one of the third list and the second list to the first list based on an experiment event outcome; and

transmitting over a network, from a computer, messages in the first list for display at a recipient computer.

8. The computer readable medium of claim 7, where dividing the ranked group of online messages into the promotion set includes moving into the promotion set only those messages having a quantity of impressions that is less than a predetermined threshold number of impressions.

9. The computer readable medium of claim 7, where the confidence value for a first message is a function of a quantity of impressions for that message.

10. The computer readable medium of claim 9, where the confidence value C₁ for the first message is determined according to the equation

C₁=tan h(imp/b)

where

imp is an average number of impressions,

b is a predetermined number of aggregations in that a b parameter may be a parameter that controls how much historical data may be needed to trust an nCTR score,

tan h is a hyperbolic tangent function, and

C₁ is a message confidence value for a given message.

11. The computer readable medium of claim 7, where the confidence value for a first message is a function of a statistical distribution of a click-through rate for that message.

12. The computer readable medium of claim 11, where the confidence value C₂ for the first message is determined according to the equation

C₂=Variance(P(θ

|α

, β

, n, k))

where

α

, β

are two positive shape parameters that parameterize a continuous probability distribution defined on an interval [0, 1],

n is a number of observed impressions made by a message,

k is a number of observed click-throughs received by a message (both n and k refer to a page message pair),

θ

is a click-through rate (CTR) of a given page-message pair and is a random variable that corresponds to an underlying CTR for a page message pair,

P(θ

|α

, β

, n, k) is a posterior distribution of a CTR given a prior Beta (alpha, beta) and an observations n and k,

Variance of a random variable or distribution (P(θ

|α

, β

, n, k)) is an expected square deviation of that variable from its expected value or mean as a measure of an amount of variation of all scores for a variable (not just extremes which give the range), and

C₂ is a message confidence value for a given message.

13. A system to optimize selection of low performance ranked messages and high performance ranked messages for display on a network location, the system comprising:

at least one web server, comprising at least one processor and memory, to present a group of online messages ranked according to performance scores; and

a processing and matching platform, comprising at least one processor and memory, coupled to the web server to divide the ranked group of online messages into a first list, a second list, and a promotion set, wherein each message in the first list has a performance score that is greater than each performance score of messages in the second list and the promotion set, to move a message within the promotion set to a third list as a function of a confidence value, to move a message from one of the third list and the second list to the first list based on an experiment event outcome, and to transmit from the processing and matching platform messages in the first list for display at a recipient computer.

14. The system of claim 13, where dividing the ranked group of online messages into the promotion set includes moving into the promotion set only those messages having a quantity of impressions that is less than a predetermined threshold number of impressions.

15. The system of claim 13, where the confidence value for a first message is a function of a quantity of impressions for that message.

16. The system of claim 15, where the confidence value C₁ for the first message is determined according to the equation

C₁=tan h(imp/b)

where

imp is an average number of impressions,

b is a predetermined number of aggregations in that a b parameter may be a parameter that controls how much historical data may be needed to trust an nCTR score,

tan h is a hyperbolic tangent function, and

C₁ is a message confidence value for a given message.

17. The system of claim 13, where the confidence value for a first message is a function of a statistical distribution of a click-through rate for that message.

18. The system of claim 17, where the confidence value C₂ for the first message is determined according to the equation

C₂=Variance(P(θ

|α

, β

, n, k))

where

α

, β

are two positive shape parameters that parameterize a continuous probability distribution defined on an interval [0, 1],

n is a number of observed impressions made by a message,

k is a number of observed click-throughs received by a message (both n and k refer to a page message pair),

θ

is a click-through rate (CTR) of a given page-message pair and is a random variable that corresponds to an underlying CTR for a page message pair,

P(θ

|α

, β

, n, k) is a posterior distribution of a CTR given a prior Beta (alpha, beta) and an observations n and k,

Variance of a random variable or distribution (P(θ

|α

, β

, n, k)) is an expected square deviation of that variable from its expected value or mean as a measure of an amount of variation of all scores for a variable (not just extremes which give the range), and

C₂ is a message confidence value for a given message.