Predictive analysis of target behaviors utilizing RNN-based user embeddings

• US 10,558,852 B2
• Filed: 11/16/2017
• Issued: 02/11/2020
• Est. Priority Date: 11/16/2017
• Status: Active Grant

• Current Assignee: Adobe Inc.
• Original Assignee: Adobe Inc.
• Inventors: Kim, Sungchul, Jain, Deepali, Gupta, Deepali, Koh, Eunyee, Kveton, Branislav, Sheoran, Nikhil, Sinha, Atanu, Bui, Hung Hai, Chen, Charles Li
• Primary Examiner(s): Desire, Gregory M
• Application Number:
  US15/814,979
• Publication Number:
  US 20190147231A1
• Time in Patent Office: 817 Days
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

Claims

1. A computer-implemented method for generating next-user-action predictive models using navigation sequences, the method comprising:

obtaining a set of navigation sequences associated with a set of users, each navigation sequence in the set of navigation sequences including a set of user actions sequentially performed during a navigation session, and each navigation sequence being associated with a user included in the set of users;

applying a Recurrent Neural Network (RNN) to the set of navigation sequences to encode each navigation sequence in the set of navigation sequences into a user embedding that reflects a temporally-defined navigation pattern for the associated user; and

applying a classifier to the user embeddings to create a next-user-action predictive model for predicting next-actions of users.

2. The computer-implemented method of claim 1, wherein the RNN is a Time-Aware RNN.

3. The computer-implemented method of claim 1, wherein the RNN is a Time-Aware Bi-Directional RNN.

4. The computer-implemented method of claim 1, wherein the RNN includes Long Short Term Memory architecture.

5. The computer-implemented method of claim 1, wherein the set of navigation sequences is obtained as a series of tuples, each tuple comprising (1) an identifier of a user action taken at a particular point in time, (2) an identifier associated with a user that performed the user action taken, and (3) an indicator of an amount of time spent on the user action taken.

6. The computer-implemented method of claim 1, further comprising:

obtaining a navigation sequence from a first user; and

applying the next-user-action predictive model to the navigation sequence to predict a probable next-action of the first user.

7. The computer-implemented method of claim 1, further comprising identifying one or more subsets of users, each subset including users included in the set of users that are associated with a similar temporally-defined navigation pattern.

8. The computer-implemented method of claim 7, wherein the one or more subsets of users are identified utilizing K-means clustering.

9. The computer-implemented method of claim 7, further comprising:

obtaining a navigation sequence from a first user;

determining that one or more of the user embeddings associated with the first user are sufficient to predict a probable next-action of the first user; and

applying the next-user-action predictive model to the one or more user embeddings associated with the first user to predict a probable next-action of the first user.

10. The computer-implemented method of claim 7, further comprising:

obtaining a navigation sequence from a first user;

determining that one or more of the user embeddings associated with the first user are insufficient to predict a probable next-action of the first user; and

applying the next-action predictive model to the one or more user embeddings associated with the first user and to user embeddings associated with other users included in a subset of users with which the first user has been identified to predict a probable next-action of the first user.

11. A computer system comprising:

one or more processors; and

one or more computer storage media storing computer useable instructions to cause the one or more processors to;

obtain a set of navigation sequences associated with a set of users, each navigation sequence in the set of navigation sequences including a set of user actions sequentially performed during a navigation session, each navigation sequence being associated with a user included in the set of users, and each navigation sequence being obtained as a series of tuples, each tuple comprising (1) an identifier of a user action taken at a particular point in time, (2) an identifier associated with a user that performed the user action taken, and (3) an indicator of an amount of time spent on the user action taken;

apply a Time-Aware Recurrent Neural Network (RNN) to the set of navigation sequences to encode each navigation sequence in the set of navigation sequences into a user embedding that reflects a temporally-defined navigation pattern for the associated user; and

apply a classifier trained to a specific target action to the user embeddings to create a next-user-action predictive model for predicting the probability that a next-action performed by a target user is the target action.

12. The computer system of claim 11, wherein the one or more processors are further caused to identify subsets of users, each subset including users included in the set of users that are associated with a similar temporally-defined navigation pattern.

13. The computer system of claim 12, wherein the one or more processors are caused to identify the subsets of users utilizing K-means clustering.

14. The computer system of claim 12, wherein the one or more processors are further caused to:

obtain a navigation sequence from a first user;

determine that one or more of the user embeddings associated with the first user are sufficient to predict a probability that a next-action performed by the first user is the target action; and

apply the next-user-action predictive model to the one or more user embeddings associated with the first user to predict a probability that the next-action performed by the first user is the target action.

15. The computer system of claim 12, wherein the one or more processors further are caused to:

obtain a navigation sequence from a first user;

determine that one or more of the user embeddings associated with the first user are insufficient to predict a probability that a next-action performed by the first user is the target action; and

apply the next-user-action predictive model to the one or more user embeddings associated with the first user and to user embeddings associated with other users included in a subset of users with which the first user has been identified to predict a probability that the next-action performed by the first user is the target action.

16. A computer-implemented method for generating next user-action predictive models using navigation sequences, the method comprising:

means for obtaining a set of navigation sequences associated with a set of users, each navigation sequence in the set of navigation sequences including a set of user actions performed during a navigation session, each navigation sequence being associated with a user included in the set of users, and each navigation sequence containing time-based information for each action in the set of user actions;

means for applying a Recurrent Neural Network (RNN) to the set of navigation sequences to encode each navigation sequence in the set of navigation sequences into a user embedding that reflects a temporally-defined navigation pattern for the associated user, the RNN being a Time-Aware RNN and including Long Short Term Memory architecture; and

means for applying a classifier to the user embeddings to create a next-user-action predictive model for predicting next-actions of users.

17. The computer-implemented method of claim 16, wherein the classifier is a target-action classifier that predicts a probability that a next-action performed by a target user is a specific target-action.

18. The computer-implemented method of claim 16, wherein the means for obtaining the set of navigation sequences associated with the set of users includes means for obtaining the set of navigation sequences as a series of tuples, each tuple comprising (1) an identifier of a user action taken at a particular point in time, (2) an identifier associated with a user that performed the user action taken, and (3) an indicator of an amount of time spent on the user action taken.

19. The computer-implemented method of claim 16, further comprising means for identifying one or more subsets of users, each subset including users included in the set of users that are associated with a similar temporally-defined navigation pattern.

20. The computer-implemented method of claim 19, wherein the means for identifying one or more subsets of users identifies the one or more subsets of users utilizing K-means clustering.