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.
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. 30. The non-transitory computer readable storage medium of claim 23 , wherein the utilizing comprises transmitting a personalized recommendation to a user based on predicting that the second user has the predicted user attribute of the associated probability.