Predicting users' attributes based on users' behaviors
First Claim
Patent Images
1. A computer-implemented method comprising:
- collecting a plurality of sample user behaviors and a plurality of sample user attributes from a service that offers videos for viewing, wherein a sample user behavior is based on a sample user using the service and a sample user attribute is received from the sample user;
training a model to be able to produce the plurality of sample user attributes from the plurality of sample user behaviors;
after training, inputting the plurality of sample user behaviors into the trained model to produce predicted sample user attributes;
comparing the plurality of sample user attributes and the plurality of sample user behaviors to the predicted sample user attributes to determine associated probabilities for the predicted sample user attributes;
determining a real user behavior for a second user based on the second user using the service;
predicting, using the model, that the second user has a predicted user attribute of an associated probability based on the user having the real user behavior, wherein the predicted user attribute of the associated probability is not known for the second user; and
utilizing that the second user has the predicted user attribute of the associated probability to improve the second user'"'"'s experience using the service.
1 Assignment
0 Petitions
Accused Products
Abstract
A method, apparatus, system, article of manufacture, and computer readable storage medium provide the ability to predict and utilize a user'"'"'s attributes. A sample user behavior and a sample user attribute are collected. A model is trained based on the sample user behavior and sample user attribute. Using the model, a probability of a predicted user attribute based on the sample user behavior is predicted. Using the model and the probability, the predicted user attribute is fuzzily determined based on a real user behavior. The predicted user attribute is used to improve a user'"'"'s experience.
-
Citations
32 Claims
-
1. A computer-implemented method comprising:
-
collecting a plurality of sample user behaviors and a plurality of sample user attributes from a service that offers videos for viewing, wherein a sample user behavior is based on a sample user using the service and a sample user attribute is received from the sample user; training a model to be able to produce the plurality of sample user attributes from the plurality of sample user behaviors; after training, inputting the plurality of sample user behaviors into the trained model to produce predicted sample user attributes; comparing the plurality of sample user attributes and the plurality of sample user behaviors to the predicted sample user attributes to determine associated probabilities for the predicted sample user attributes; determining a real user behavior for a second user based on the second user using the service; predicting, using the model, that the second user has a predicted user attribute of an associated probability based on the user having the real user behavior, wherein the predicted user attribute of the associated probability is not known for the second user; and utilizing that the second user has the predicted user attribute of the associated probability to improve the second user'"'"'s experience using the service. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
-
-
13. An apparatus for utilizing a user'"'"'s predicted attributes in a computer system comprising:
-
(a) a computer having a memory; (b) an application executing on the computer, wherein the application is configured to; collect a plurality of sample user behaviors and a plurality of sample user attributes from a service that offers videos for viewing, wherein a sample user behavior is based on a sample user using the service and a sample user attribute is received from the sample user; train a model to be able to produce the plurality of sample user attributes from the plurality of sample user behaviors; after training, input the plurality of sample user behaviors into the trained model to produce predicted sample user attributes; compare the plurality of sample user attributes and the plurality of sample user behaviors to the predicted sample user attributes to determine associated probabilities for the predicted sample user attributes; determine a real user behavior for a second user based on the second user using the service; predict, using the model, that the second user has a predicted user attribute of an associated probability based on the user having the real user behavior, wherein the predicted user attribute of the associated probability is not known for the second user; and utilize that the second user has the predicted user attribute of the associated probability to improve the second user'"'"'s experience using the service. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26)
-
-
23. A non-transitory computer readable storage medium encoded with computer program instructions which when accessed by a computer cause the computer to load the program instructions to a memory therein creating a special purpose data structure causing the computer to operate as a specially programmed computer, executing a method of utilizing a user'"'"'s predicted attributes, comprising:
-
collecting, in the specially programmed computer, a plurality of sample user behaviors and a plurality of sample user attributes from a service that offers videos for viewing, wherein a sample user behavior is based on a sample user using the service and a sample user attribute is received from the sample user; training, in the specially programmed computer, a model to be able to produce the plurality of sample user attributes from the plurality of sample user behaviors; after training, inputting, in the specially programmed computer, the plurality of sample user behaviors into the trained model to produce predicted sample user attributes; comparing, in the specially programmed computer, the plurality of sample user attributes and the plurality of sample user behaviors to the predicted sample user attributes to determine associated probabilities for the predicted sample user attributes; determining, in the specially programmed computer, a real user behavior for a second user based on the second user using the service; predicting, in the specially programmed computer, in the specially programmed computer, using the model, that the second user has a predicted user attribute of an associated probability based on the user having the real user behavior, wherein the predicted user attribute of the associated probability is not known for the second user; and utilizing, in the specially programmed computer, that the second user has the predicted user attribute of the associated probability to improve the second user'"'"'s experience using the service. - View Dependent Claims (27, 28, 29, 30, 31, 32)
-
Specification