Predictive analysis of target behaviors utilizing RNN-based user embeddings
View Patent ↗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.
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.