TIME DISCOUNT RATE ESTIMATION APPARATUS, MACHINE LEARNING METHOD, TIME DISCOUNT RATE ANALYSIS METHOD, AND PROGRAM
An object of the present disclosure is to accurately estimate a time discount rate of a user without depending on a measurement method using questionnaires. Therefore, the present disclosure provides a time discount rate estimation device that estimates a time discount rate in a learning phase, the time discount rate estimation device including: a behavior transition time calculation unit that calculates a transition time from a behavior of a predetermined user recorded at each date and time to all types of behaviors of the predetermined user and outputs behavior transition time feature data for each behavior recorded at each date and time; and a time discount rate estimation model learning unit that calculates an error between a value of a time discount rate obtained by inputting the behavior transition time feature data to a time discount rate estimation model obtained by deep learning and a time discount rate serving as correct answer data based on an answer by the predetermined user and performs machine learning on the time discount rate estimation model so as to reduce the error.
1 . A time discount rate estimation apparatus that estimates a time discount rate in a learning phase, the time discount rate estimation apparatus comprising:
a processor; and
a memory that includes instructions, which when executed, cause the processor to execute:
calculating a transition time from a behavior of a predetermined user recorded at each date and time to all types of behaviors of the predetermined user and outputs behavior transition time feature data for each behavior recorded at each date and time; and
calculating an error between a value of a time discount rate obtained by inputting the behavior transition time feature data to a time discount rate estimation model obtained by deep learning and a time discount rate serving as correct answer data based on an answer by the predetermined user and performing machine learning on the time discount rate estimation model so as to reduce the error.
2 . The time discount rate estimation apparatus according to claim 1 , wherein
the plurality of behavior features of the predetermined user is based on behavior data observed by a wearable device worn by the predetermined user.
3 . The time discount rate estimation apparatus according to claim 1 , further comprising
performing preprocessing of behavior data indicating the behavior of the predetermined user by deleting, from the behavior data, data regarding behaviors of the same type successively observed in a predetermined time, then giving unique behavior identification information associated with the type of behavior, and associating the behavior identification information with the behavior transition time feature data.
4 . A time discount rate estimation apparatus that estimates a time discount rate in an estimation phase, the time discount rate estimation apparatus comprising:
a processor; and
a memory that includes instructions, which when executed, cause the processor to execute:
calculating a time discount rate based on behavior data indicating a behavior of a specific user recorded at each date and time and outputting the time discount rate by using a machine-learned time discount rate estimation model obtained by calculating an error between a value of a time discount rate obtained by inputting behavior transition time feature data indicating a transition time from a behavior of a predetermined user recorded at each date and time to all types of behaviors of the predetermined user to a time discount rate estimation model obtained by deep learning and a time discount rate serving as correct answer data based on an answer by the predetermined user and performing machine learning so as to reduce the error.
5 . The time discount rate estimation apparatus according to claim 4 , wherein:
the time discount rate estimation model includes a self-attention that calculates a weight for each transition time; and
the instructions, which when executed, further cause the processor to execute:
visualizing importance of the behavior of the specific user recorded at each date and time based on the weight for each transition time and outputting the importance.
6 . A machine learning method of performing machine learning on a time discount rate estimation model for estimating a time discount rate in a learning phase, the machine learning method comprising:
calculating a transition time from a behavior of a predetermined user recorded at each date and time to all types of behaviors of the predetermined user and outputting behavior transition time feature data for each behavior recorded at each date and time, and
calculating an error between a value of a time discount rate obtained by inputting the behavior transition time feature data to a time discount rate estimation model obtained by deep learning and a time discount rate serving as correct answer data based on an answer by the predetermined user and performing machine learning on the time discount rate estimation model so as to reduce the error.
7 . A time discount rate estimation method of estimating a time discount rate in an estimation phase, the time discount rate estimation method comprising:
calculating a time discount rate based on behavior data indicating a behavior of a specific user recorded at each date and time and outputting the time discount rate, by using a machine-learned time discount rate estimation model obtained by calculating an error between a value of a time discount rate obtained by inputting behavior transition time feature data indicating a transition time from a behavior of a predetermined user recorded at each date and time to all types of behaviors of the predetermined user to a time discount rate estimation model obtained by deep learning and a time discount rate serving as correct answer data based on an answer by the predetermined user and performing machine learning so as to reduce the error.
8 . A non-transitory computer-readable recording medium having computer-readable instructions stored thereon, which when executed, cause a computer to execute the method according to claim 6 .