SYSTEM AND METHOD FOR DETERMINING AN EVENT OCCURENCE RATE

• US 20080250033A1
• Filed: 04/05/2007
• Published: 10/09/2008
• Est. Priority Date: 04/05/2007
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Chakrabarti, Deepayan, Josifovski, Vanja, Agarwal, Deepak, Diklic, Dejan, Border, Andrei Zary
• Granted Patent: US 7,921,073 B2
• Time in Patent Office: Days
• US Class Current: 707/100
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Described are a system and method for determined an event occurrence rate. A sample set of content items may be obtained. Each of the content items may be associated with at least one region in a hierarchical data structure. A first impression volume may be determined for the at least one region as a function of a number of impressions registered for the content items associated with the at least one region. A scale factor may be applied to the first impression volume to generate a second impression volume. The scale factor may be selected so that the second impression volume is within a predefined range of a third impression volume. A click-through-rate (CTR) may be estimated as a function of the second impression volume and a number of clicks on the content item.

Claims

1. A method, comprising:

obtaining a sample set of content items, each of the content items being associated with at least one region in a hierarchical data structure, a hierarchical data structure comprising nodes in an advertisement taxonomy hierarchy and nodes in a page taxonomy hierarchy, with the at least one region identified by a combination of nodes from the advertisement taxonomy hierarchy and nodes from the page taxonomy hierarchy;

determining a first impression volume for the at least one region as a function of a number of impressions registered for the content items associated with the at least one region;

applying a scale factor to the first impression volume to generate a second impression volume, the scale factor being selected so that the second impression volume is within a predefined range of a third impression volume;

estimating a click-through-rate (CTR) as a function of the second impression volume and a number of clicks on the content item.

2. The method according to claim 1, wherein the content items include at least one of webpages and ads.

3. The method according to claim 1, wherein the obtaining includes:

identifying first content items that have been clicked;

identifying a predetermined number of second content items that have not been clicked; and

generating the sample set as a function of the first and second content items.

4. The method according to claim 3, further comprising:

calculating the first impression volume as a function of the impressions for the first and second content items.

5. The method according to claim 1, wherein the third impression volume is a total number of impressions associated within a preselected level in the hierarchical data structure.

6. The method according to claim 1, further comprising:

computing a difference impression volume as a difference between the first impression volume and the third impression volume; and

distributing the difference impression volume to the at least one region as a function of the first impression volume.

7. The method according to claim 6, wherein the distributing includes:

determining a sum of the first impression volumes for each region across a level of the hierarchical data structure; and

computing a ratio of the first impression volume for a given region to the sum; and

multiplying the difference impression volume by the ratio to determine an impression addition for the given region; and

adding the impression addition to the first impression volume of the given region to generate a fourth impression volume.

8. The method according to claim 1, wherein the estimating includes:

assigning a state variable to each of the at least one region; and

applying a Markovian model to the state variable to estimate the CTR.

9. The method according to claim 8, wherein the applying includes:

computing a posterior for the state using a Kalman filter; and

propagating the posterior to the at least one region; and

repeating the computing and the propagating until convergence.

10. The method according to claim 9, further comprising:

upon the convergence, identifying the CTR for the at least one region.

11. The method according to claim 1, further comprising:

storing the CTR on a storage medium.