Enhanced online content delivery system using action rate lift

Patent metadata

Field	Value
Publication number	US-2016189207-A1
Application number	US-201514740937-A
Country	US
Kind code	A1
Filing date	Jun 16, 2015
Priority date	Dec 26, 2014
Publication date	Jun 30, 2016
Grant date	—

Abstract

Described herein are example systems and operations for enhancing targeted delivery of online content using action rate lift and/or A/B testing. These examples provide solutions to problems in targeted delivery of online content, such as the problem of not being able to identify audience and/or situational targets mostly or only influenced by the content item or campaign of concern. For example, described herein are solutions that can estimate AR lift associated with a content item, and then distribute the content item or similar content items accordingly. An AR lift model can be used and such a model can use machine learning, A/B testing, and/or statistical analysis.

First claim

1 . A system for enhanced targeted distribution of online content, comprising:
action rate (AR) lift circuitry, configured to estimate an AR lift associated with a corresponding action and an online content item, the AR lift circuitry including: AR-without-item sub-circuitry configured to estimate a first AR based on a first assumption that the content item is not distributed to a given user in response to a request by the given user; AR-with-item sub-circuitry configured to estimate a second AR based on a second assumption that the content item is distributed to the given user in response to the request; and AR-lift sub-circuitry configured to estimate the AR lift by determining a difference between the first AR and the second AR; and distribution circuitry, configured to control distribution of the online content item over the Internet based on the AR lift determined by the AR-lift sub-circuitry and a cost per action associated with the corresponding action and the online content item.

2 . The system of claim 1 , wherein:
the AR-without-item sub-circuitry is further configured to: receive the request from the given user; receive content provider information including an indication of the corresponding action; and estimate the first AR as a probability that the given user performs the corresponding action based at least on a state of the given user, where the content item is not served in response to the request; and the AR-with-item sub-circuitry is further configured to: receive the request from the given user; receive the content provider information indicating the corresponding action; and estimate the second AR as a probability that the given user performs the corresponding action based at least on the state of the given user, where the content item is served in response to the request.

3 . The system of claim 2 , further comprising user state circuitry configured to:
update states of users of the system periodically.

4 . The system of claim 2 , further comprising user state circuitry configured to:
map the state of the given user to a set of features that are shared among different users of the system; and communicate the mapped state to the AR-without-item sub-circuitry and the AR-with-item sub-circuitry, wherein the AR-without-item sub-circuitry and the AR-with-item sub-circuitry use the mapped state as a basis for their respective estimations.

5 . The system of claim 2 , wherein the state of the given user includes demographic or psychographic information pertaining to the given user.

6 . The system of claim 2 , wherein the state of the given user is a state of the user at a time of the request.

7 . The system of claim 1 , further comprising machine learning circuitry configured to interact with the AR-without-item sub-circuitry and the AR-with-item sub-circuitry to provide machine learning operations for the respective estimations of the AR-without-item sub-circuitry and the AR-with-item sub-circuitry.

8 . The system of claim 7 , wherein the machine learning operations include a boosting method.

9 . The system of claim 8 , wherein the machine learning operations include a gradient boosting decision tree.

10 . The system of claim 1 , wherein the distribution circuitry is further configured to:
determine a bid price to acquire an impression of the content item in response to the request, based on the AR lift estimated by the AR-lift sub-circuitry; and control distribution of the online content item over the Internet based on the bid price.

11 . The system of claim 1 , further comprising averaging circuitry, including:
AR-averaging sub-circuitry configured to determine an average AR for a plurality of users based on respective estimations of the second AR for the plurality of users by the AR-with-item sub-circuitry; and AR-lift-averaging sub-circuitry configured to determine an average AR lift for the plurality of users based on respective estimations of the AR lift for the plurality of users by the AR-lift sub-circuitry, and wherein the distribution circuitry is configured to control distribution of the online content item over the Internet based on an average AR determined by the AR-averaging sub-circuitry, an average AR lift determined by the AR-lift-averaging sub-circuitry, and the cost per action.

12 . The system of claim 11 , wherein the distribution circuitry is further configured to:
determine a bid price to acquire an impression of the content item in response to the request based on the average AR lift for the plurality of users determined by the AR-lift-averaging sub-circuitry; and control distribution of the online content item over the Internet based on the bid price.

13 . A method for enhanced targeted distribution of online content, comprising:
receiving, at network interface circuitry, a Hypertext Transfer Protocol (HTTP) request from a given user, via a browser; receiving, at the network interface circuitry, content provider information; estimating, by action rate (AR) lift circuitry, an AR lift associated with a corresponding action and an online content item, the content provider information including an indication of the corresponding action, and the estimating of the AR lift including: estimating a first AR based on a first assumption that the content item is not distributed to the given user in response to the request, the estimating of the first AR including estimating a probability that the given user performs the corresponding action based at least on a state of the given user; estimating a second AR based on a second assumption that the content item is distributed to the given user in response to the request, the estimating of the second AR including estimating a probability that the given user performs the corresponding action based at least on the state of the given user; and estimating the AR lift according to a difference between the first AR and the second AR; and controlling, by distribution circuitry, distribution of the online content item over the Internet based on the AR lift and a cost per action associated with the corresponding action and the online content item.

14 . The method of claim 13 , further comprising determining, by user state circuitry, the state of the given user by mapping the state to a set of features that are shared among a predetermined set of users similar to the given user, the predetermination based on a user similarity function, and the determination of the state of the given user occurring subsequent to the request.

15 . The method of claim 13 , wherein the state of the given user includes demographic or psychographic information pertaining to the given user.

16 . The method of claim 13 , wherein the state of the given user is a state of the user at a time of the request.

17 . The method of claim 13 , wherein the state of the given user is updated according to a predetermined schedule.

18 . The method of claim 13 , further comprising:
determining a bid price to acquire an impression of the content item in response to the request, based on the AR lift; and controlling the distribution of the online content item over the Internet based on the bid price.

19 . The method of claim 13 , further comprising:
determining, by averaging circuitry, an average AR for a plurality of users based on respective estimations of the second AR for the plurality of users; determining, by the averaging circuitry, an average AR lift for the plurality of users based on respective estimations of the AR lift for the plurality

Assignees

• Yahoo Inc

Inventors

• Xu Jian
• Shao Xuhui
• Ma Jianjie
• Lu Quan

Classifications

• G06Q30/0277

  Online advertisement · CPC title

• G06Q30/0269

  based on user profile or attribute · CPC title

• G06Q30/0275

  Auctions · CPC title

• G06Q30/0264

  based upon schedule · CPC title

• G06Q30/0249 Primary

  based upon budgets or funds · CPC title