User action prediction

• US 10,643,141 B2
• Filed: 09/09/2015
• Issued: 05/05/2020
• Est. Priority Date: 09/09/2015
• Status: Active Grant

• Current Assignee: Yahoo Assets LLC
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Laptev, Nikolay Pavlovich, Shu, Xiaokui
• Primary Examiner(s): Wong, Lut
• Application Number:
  US14/849,008
• Publication Number:
  US 20170068899A1
• Time in Patent Office: 1,700 Days
• Field of Search: 706 46
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A webpage navigation of a user over a timeframe and a second webpage navigation of a second user over a second timeframe may be received. A time-variant variable-order Markov model, comprising a context tree, may be generated utilizing the webpage navigation and the second webpage navigation. A third webpage navigation of a third user may be received. A probability that the third user may interact with content, that the third user is a non-human entity, and/or that the third user will access a website may be determined based upon an evaluation of the third webpage navigation using the time-variant variable-order Markov model. A second client device is instructed to present the content to the third user, to present a human verification mechanism to the third user, and/or to instruct a server, providing the website, to alter a server capacity for the website.

Claims

1. A client device for user action prediction, the client device comprising:

a processor;

a display; and

a memory storing instructions that, when executed on the processor, provide a prediction component configured to;

receive a webpage navigation of a user over a timeframe;

receive a second webpage navigation of a second user over a second timeframe, the second timeframe overlapping the timeframe;

generate a time-variant variable-order model, comprising a context tree, utilizing the webpage navigation and the second webpage navigation;

receive a third webpage navigation of a third user;

determine a probability that the third user will interact with content based upon an evaluation of the third webpage navigation using the time-variant variable-order model;

responsive to the probability exceeding a threshold, instruct a second client device to present the content to the third user; and

responsive to predicting that more than a threshold number of a plurality of users are associated with a probability of interacting with the content that exceeds a threshold probability based upon a determination that a second threshold number of users visited one or more webpages associated with a likelihood of visiting the content, predict increased traffic for a server providing the content and instruct the server to increase a server capacity for the content, wherein the one or more webpages associated with the likelihood of visiting the content are different than the content.

2. The client device of claim 1, the time-variant variable-order model comprising a Markov model.

3. The client device of claim 1, the prediction component configured to:

receive an extraction of the webpage navigation of the user;

transform the webpage navigation into a format; and

load the webpage navigation, according to the format, into the time-variant variable-order model.

4. The client device of claim 1, the prediction component configured to:

receive an extraction of the second webpage navigation of the second user;

transform the second webpage navigation into a format; and

load the second webpage navigation into the time-variant variable-order model.

5. The client device of claim 1, the prediction component configured to generate the time-variant variable-order model utilizing:

in window aggregation, the in window aggregation outputting;

a webpage location of the user, based upon the webpage navigation, for one or more time points; and

a second webpage location of the second user, based upon the second webpage navigation, for one or more second time points.

6. The client device of claim 1, the time-variant variable-order model configured to at least one of:

identify a first trend indicating the third user has a decreased probability of interacting with the content;

or

identify a second trend indicating the third user has an increased probability of interacting with the content.

7. The client device of claim 1, the prediction component configured to:

prune a branch of the context tree based upon the branch corresponding to a first type of node.

8. A method for user action prediction, comprising:

receiving a webpage navigation of a user over a timeframe;

receiving a second webpage navigation of a second user over a second timeframe, the second timeframe overlapping the timeframe;

generating a time-variant variable-order model, comprising a context tree, utilizing the webpage navigation and the second webpage navigation;

receiving a third webpage navigation of a third user;

determining a probability that the third user is a non-human entity based upon an evaluation of the third webpage navigation using the time-variant variable-order model;

responsive to the probability exceeding a threshold, identifying the third user as the non-human entity; and

responsive to predicting that more than a threshold number of a plurality of users are associated with a probability of interacting with content that exceeds a threshold probability, predicting increased traffic for a server providing the content and instructing the server to increase a server capacity for the content.

9. The method of claim 8, the time-variant variable-order model comprising a Markov model.

10. The method of claim 8, comprising:

receiving an extraction of the webpage navigation of the user;

transforming the webpage navigation into a format; and

loading the webpage navigation, according to the format, into the time-variant variable-order model.

11. The method of claim 8, comprising:

receiving an extraction of the second webpage navigation of the second user;

transforming the second webpage navigation into a format; and

loading the second webpage navigation, according to the format, into the time-variant variable-order model.

12. The method of claim 8, comprising:

utilizing in window aggregation to output;

a webpage location of the user, based upon the webpage navigation, for one or more time points; and

a second webpage location of the user, based upon the second webpage navigation, for one or more second time points.

13. The method of claim 8, comprising:

pruning a branch of the context tree based upon the branch corresponding to a first type of node.

14. The method of claim 8, comprising:

responsive to identifying the third user as the non-human entity, presenting the third user with a human verification mechanism.

15. A client device for user action prediction, the client device comprising:

a processor;

a display; and

a memory storing instructions that, when executed on the processor, provide a prediction component configured to;

receive a webpage navigation of a user over a timeframe;

receive a second webpage navigation of a second user over a second timeframe, the second timeframe overlapping the timeframe;

generate a time-variant variable-order model, comprising a context tree, utilizing the webpage navigation and the second webpage navigation;

receive webpage navigations of a plurality of users;

determine a probability that each user of the plurality of users will interact with a website based upon an evaluation of at least one webpage navigation, of the webpage navigations, associated with the user using the time-variant variable-order model; and

responsive to predicting that more than a threshold number of the plurality of users are associated with a probability of interacting with the website that exceeds a threshold probability based upon a determination that a second threshold number of users visited one or more webpages associated with a likelihood of visiting the website, predict increased traffic for a server providing the website and instruct the server to alter a server capacity for the website, wherein the one or more webpages associated with the likelihood of visiting the website are different than the website.

16. The client device of claim 15, the time-variant variable-order model comprising a Markov model.

17. The client device of claim 15, the prediction component configured to:

prune a branch of the context tree based upon the branch corresponding to a first type of node.

18. The client device of claim 15, the prediction component configured to:

receive an extraction of the webpage navigation of the user;

transform the webpage navigation into a format;

load the webpage navigation, according to the format, into the time-variant variable-order model;

receive an extraction of the second webpage navigation of the second user;

transform the second webpage navigation into the format; and

load the second webpage navigation, according to the format, into the time-variant variable-order model.

19. The client device of claim 15, the prediction component configured to generate the time-variant variable-order model utilizing:

in window aggregation, the in window aggregation outputting;

a webpage location of the user, based upon the webpage navigation, for one or more time points; and

a second webpage location of the user, based upon the second webpage navigation, for one or more second time points.

20. The client device of claim 15, the time-variant variable-order model configured to at least one of:

identify a first trend indicating a third user has a decreased probability of interacting with the website;

or

identify a second trend indicating the third user has an increased probability of interacting with the website.