Method, computer device, and non-transitory computer-readable recording medium for providing optimal path

Abstract

A method for providing an optimal path to achieve a user goal includes collecting user historical session data that includes a user action trajectory based on a session unit; and generating a model for optimal path prediction by representing the user historical session data as a path in a form of a graph and by learning the path.

What is claimed is: 
     
         1 . A method for providing an optimal path performed by a computer device having at least one processor configured to execute computer-readable instructions included in a memory, the method comprising:
 collecting user historical session data that includes a user action trajectory based on a session unit; and   generating a model for optimal path prediction by representing the user historical session data as a path in a form of a graph and by learning the path.   
     
     
         2 . The method of  claim 1 , wherein the collecting of the user historical session comprises collecting a series of user experiences in a corresponding session as a set of sample data for each session. 
     
     
         3 . The method of  claim 1 , wherein the collecting of the user historical session comprises collecting the user historical session data of the session unit as a user log for a service that is used by a user among a plurality of services connected over a network. 
     
     
         4 . The method of  claim 1 , wherein the generating of the model comprises representing the user historical session data as at least one path based on the session unit, and
 the path includes a state at each time step, an action in the state, and a reward for the action.   
     
     
         5 . The method of  claim 4 , wherein the state is defined with contents related to a service screen consumed by a user and further includes at least one of a service type, user-related environmental information, user's personal information, and a session category,
 the action is defined as a user activity in the state, and   the reward is defined as a user satisfaction for the action.   
     
     
         6 . The method of  claim 4 , wherein the reward is determined based on a feedback that is directly received from a user for the action. 
     
     
         7 . The method of  claim 4 , wherein the reward is determined based on at least one of a dwell time for a state according to the action and an additional action. 
     
     
         8 . The method of  claim 4 , wherein the model for optimal path prediction is generated based on at least one of reinforcement learning, language modeling learning, and neural network learning for the state, the action, and the reward. 
     
     
         9 . The method of  claim 1 , further comprising:
 predicting a subsequent action through the model for the optimal path prediction for a target user and recommending a path of the predicted subsequent action as an optimal path.   
     
     
         10 . The method of  claim 9 , wherein the recommending of the path comprises predicting the subsequent action using user historical session data that includes a previous action trajectory in a current session of the target user. 
     
     
         11 . A non-transitory computer-readable recording medium storing instructions that, when executed by a processor, cause the processor to computer-implement the optimal path providing method of  claim 1 . 
     
     
         12 . A computer device for providing an optimal path, comprising:
 at least one processor configured to execute computer-readable instructions included in a memory,   wherein the at least one processor is configured to execute:   a process of collecting user historical session data that includes a user action trajectory based on a session unit; and   a process of generating a model for optimal path prediction by representing the user historical session data as a path in a form of a graph and by learning the path.   
     
     
         13 . The computer device of  claim 12 , wherein the at least one processor is configured to collect a series of user experiences in a corresponding session as a set of sample data for each session. 
     
     
         14 . The computer device of  claim 12 , wherein the at least one processor is configured to collect the user historical session data of the session unit as a user log for a service that is used by a user among a plurality of services connected over a network. 
     
     
         15 . The computer device of  claim 12 , wherein the at least one processor is configured to process a process of representing the user historical session data as at least one path based on the session unit, and
 the path includes a state at each time step, an action in the state, and a reward for the action.   
     
     
         16 . The computer device of  claim 15 , wherein the state is defined with contents related to a service screen consumed by a user and further includes at least one of a service type, user-related environmental information, user's personal information, and a session category,
 the action is defined as a user activity in the state, and   the reward is defined as a user satisfaction for the action.   
     
     
         17 . The computer device of  claim 15 , wherein the reward is determined based on at least one of a user feedback for the action, a dwell time for a state according to the action, and an additional action. 
     
     
         18 . The computer device of  claim 15 , wherein the at least one processor is configured to generate the model for optimal path prediction based on at least one of reinforcement learning, language modeling learning, and neural network learning for the state, the action, and the reward. 
     
     
         19 . The computer device of  claim 12 , wherein the at least one processor is configured to predict a subsequent action through the model for the optimal path prediction for a target user and to recommend a path of the predicted subsequent action as an optimal path. 
     
     
         20 . The computer device of  claim 19 , wherein the at least one processor is configured to predict the subsequent action using user historical session data that includes a previous action trajectory in a current session of the target user.