Online user space exploration for recommendation

Patent metadata

Field	Value
Publication number	US-10467313-B2
Application number	US-201715459633-A
Country	US
Kind code	B2
Filing date	Mar 15, 2017
Priority date	Mar 15, 2017
Publication date	Nov 5, 2019
Grant date	Nov 5, 2019

Abstract

To maximize the accuracy and efficiency of predicting users that will enjoy targeted content, a proposed content selection solution looks to combine a first strategy of utilizing selection rules with a second strategy of utilizing machine based learning models. By combining the selection rules-based approach and the machine learning model-based approach, the proposed content selection solution is able to consider and recommend a wider range of users for each available content.

First claim

What is claimed is:

1. A computing device comprising:
a communication interface configured to receive user information including user online interaction data;
a memory configured to store a selection rule describing parameters for including one or more users into a user set;
a processor configured to:
receive the user information through the communication interface;
retrieve the selection rule from the memory;
create a first predictor protocol grouping one or more users into a first user set based on the selection rule;
create a second predictor protocol grouping one or more users into a second user set based on a machine learning technique; and
generate a user set proposal including the first user set and the second user set.

2. The computing device of claim 1 , wherein the processor is further configured to:
monitor a first performance of a first user included in the first user set interacting with a targeted content, the first performance monitoring a number of conversions of the targeted content by the first user;
monitor a second performance of a second user included in the second user set interacting with the targeted content, the second performance monitoring a number of conversions of the targeted content by the second user;
compare the first performance to the second performance; and
select one of the first user set or the second user set based on the comparison of the first performance and the second performance according to a selection strategy.

3. The computing device of claim 2 , wherein a conversion of the targeted content includes either the first user or the second user clicking on the targeted content.

4. The computing device of claim 2 , wherein the selection strategy is a multi-armed bandit selection strategy.

5. The computing device of claim 1 , wherein the processor is further configured to:
create a third user set comprised of users included in both the first user set and the second user set.

6. The computing device of claim 1 , wherein the selection rule selects users within an overall user set that satisfies at least one of a user location requirement, user attribute requirement, or user web browser history requirement.

7. The computing device of claim 1 , wherein the first user set and the second user set include users from within an overall user set monitored by a common website publisher.

8. The computing device of claim 1 , wherein the machine learning technique outputs a respective conversion score based on conversion of targeted content presented to a user; and
wherein the processor is further configured to select a user to group into the second user set when the user has a conversion score greater than a predetermined threshold value.

9. The computing device of claim 1 , wherein the processor is further configured to:
detect a user visiting a website operated by a common website publisher;
determine whether to implement a cold start scenario based on an impression count of a targeted content being greater than a predetermined threshold; and
present the targeted content to the user when determined to implement the cold start scenario.

10. The computing device of claim 9 , wherein the processor is further configured to:
implement a warm start scenario when the impression count is less than the predetermined threshold;
select either the first user set or the second user set based on a Thompson Sampling selection strategy;
determine whether the user is included in either the first user set or the second user set; and
present the targeted content to the user when the user is included in the first user set or the second user set.

11. A method comprising:
receiving, by a processor, user information through a communication interface;
retrieving, by the processor, a selection rule stored in a memory;
creating, by the processor, a first predictor protocol grouping one or more users into a first user set based on the selection rule;
creating, by the processor, a second predictor protocol grouping one or more users into a second user set based on a machine learning technique; and
generating, by the processor, a user set proposal including the first user set and the second user set.

12. The method of claim 11 , further comprising:
monitoring, by the processor, a first performance of a first user included in the first user set interacting with a targeted content, the first performance monitoring a number of conversions of the targeted content by the first user;
monitoring, by the processor, a second performance of a second user included in the second user set interacting with the targeted content, the second performance monitoring a number of conversions of the targeted content by the second user;
comparing, by the processor, the first performance to the second performance; and
selecting, by the processor, one of the first user set or the second user set based on the comparison of the first performance and the second performance according to a selection strategy.

13. The method of claim 12 , converting the targeted content includes either the first user or the second user clicking on the targeted content.

14. The method of claim 12 , wherein the selection strategy is a multi-armed bandit selection strategy.

15. The method of claim 11 , further comprising:
creating, by the processor, a third user set comprised of users included in both the first user set and the second user set.

16. The method of claim 11 , wherein the selection rule selects users within an overall user set that satisfies at least one of a user location requirement, user attribute requirement, or user web browser history requirement.

17. The method of claim 11 , wherein the first user set and the second user set include users from within an overall user set monitored by a common website publisher.

18. The method of claim 11 , wherein the machine learning technique outputs a respective conversion score based on conversion of targeted content presented to a user; and
wherein the method further comprises selecting a user to group into the second user set when the user has a conversion score greater than a predetermined threshold value.

19. The method of claim 11 , further comprising:
detecting, by the processor, a user visiting a website operated by a common website publisher;
determining, by the processor, whether to implement a cold start scenario based on an impression count of a targeted content being greater than a predetermined threshold; and
presenting, by the processor, the targeted content to the user when determined to implement the cold start scenario.

20. The method of claim 19 , further comprising:
implementing, by the processor, a warm start scenario when the impression count is less than the predetermined threshold;
selecting, by the processor, either the first user set or the second user set based on a Thompson Sampling selection strategy;
determining, by the processor, whether the user is included in either the first user set or the second user set; and
presenting, by the processor, the targeted content to the user when the user is included in the first user set or the second user set.

Assignees

• Yahoo Holdings Inc
• Oath Inc

Inventors

• Wang Liang
• Pan Shengjun
• Lee Kuang-Chih
• Lu Quan
• Pan Junwei

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F16/9535

  Search customisation based on user profiles and personalisation · CPC title

• G06F16/954 Primary

  Navigation, e.g. using categorised browsing · CPC title

• H04L67/02

  based on web technology, e.g. hypertext transfer protocol [HTTP] · CPC title