A method of predicting personality traits using a personal life log and an apparatus for performing the same are disclosed. The method of predicting personality traits comprises collecting personal life log in a social network, generating a user behavior matrix by defining an object about user's behavior through analysis of the collected personal life log in a triple structure and extracting a user behavior parameter through the generated user behavior matrix, obtaining interaction between a user and a friend by analyzing the personal life log and obtaining a friend relation characteristic parameter by using the obtained interaction, obtaining a moving path characteristic parameter by using location information made in a feed by the user through analysis of the personal life log, and predicting personality traits by applying the user behavior parameter, the friend relation characteristic parameter and the moving path characteristic parameter to four learned personality traits models.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of predicting personality traits, the method comprising: collecting personal life log in a social network; generating a user behavior matrix by defining an object about user's behavior through analysis of the collected personal life log in a triple structure and extracting a user behavior parameter through the generated user behavior matrix; obtaining interaction between a user and a friend by analyzing the personal life log and obtaining a friend relation characteristic parameter by using the obtained interaction; obtaining a moving path characteristic parameter by using location information made in a feed by the user through analysis of the personal life log; and predicting personality traits by applying the user behavior parameter, the friend relation characteristic parameter and the moving path characteristic parameter to four learned personality traits models. 2. The method of claim 1, wherein the user behavior matrix is defined by generalizing the behavior (predicate) of the user (subject) in the personal life log to the object (object), and wherein the triple structure includes the user, the object and the behavior. 3. The method of claim 1, wherein the step of obtaining the friend relation characteristic parameter includes: extracting a frequency of comment written in a user's feed by the friend and a frequency of user's response about the comment as the interaction, by analyzing the personal life log; and extracting a number of a close friend and a number of acquaintance as the friend relation characteristic parameter by dividing clusters by applying the obtained interaction to a K-average cluster algorithm. 4. The method of claim 1, wherein the step of the moving path characteristic parameter comprising: obtaining an average moving distance between visiting places by using the location information made by the user; and extracting a number of the visiting places and a visiting frequency of the visiting places as the POI variety by using the location information. 5. The method of claim 1, wherein the average moving distance and the POI variety are determined as the moving path characteristic parameter, and the location information includes at least one of a GPS coordinate, names of the visiting places or identification information (ID) of the visiting places. 6. The method of claim 1, wherein the step of predicting the personality traits includes: obtaining optimal parameter combinations about each of the four personality traits prediction models by using the user behavior parameter, the friend relation characteristic parameter and the moving path characteristic parameter; and performing a linear regression analysis by applying the obtained optimal parameter combinations to the four personality traits prediction models and predicting the personality traits through the linear regression analysis. 7. The method of claim 6, wherein the step of obtaining the optimal parameter combinations includes: obtaining the optimal parameter combinations for minimizing a root mean square error RMSE of 10-fold cross validation by using the user behavior parameter, the friend relation characteristic parameter and the moving path characteristic parameter applied to the four personality traits prediction models. 8. The method of claim 1, wherein the four personality traits prediction models include an extraversion prediction model in a consumer psychology field, a public self consciousness prediction model, a prediction model of desire for uniqueness and a self esteem prediction model. 9. The method of claim 1, further comprising: obtaining correlation between the user behavior parameter, the friend relation characteristic parameter and the moving path characteristic parameter and use date, and normalizing parameters by dividing the parameters of which the correlation is more than a first critical value by a total use period per the user; and calculating skewness of the parameters and normalizing parameters by applying a log function to the parameters of which the skewness is more than a second critical value, and wherein the normalizing is performed before the step of predicting the personality traits, and the total use period is calculated through a number of days between an initial feed generation day and a final feed generation day. 10. The method of claim 9, further comprising: learning the four personality traits models by using the user behavior parameter, the friend relation characteristic parameter and the moving path characteristic parameter. 11. A recording medium readable by a computer recording a program code performing the steps comprising: collecting personal life log in a social network; generating a user behavior matrix by defining an object about user's behavior through analysis of the collected personal life log in a triple structure and extracting a user behavior parameter through the generated user behavior matrix; obtaining interaction between a user and a friend by analyzing the personal life log and obtaining a friend relation characteristic parameter by using the obtained interaction; obtaining a moving path characteristic parameter by using location information made in a feed by the user through analysis of the personal life log; and predicting personality traits by applying the user behavior parameter, the friend relation characteristic parameter and the moving path characteristic parameter to four learned personality traits models. 12. A personality traits prediction apparatus comprising: a collection unit configured to collect personal life log in a social network; a characteristic parameter extracting unit configured to generate a user behavior matrix by defining an object about user's behavior through analysis of the collected personal life log in a triple structure and extract a user behavior parameter through the generated user behavior matrix; a friend relation analyzing unit configured to obtain interaction between a user and a friend by analyzing the personal life log and obtain a friend relation characteristic parameter by using the obtained interaction; a moving path analyzing unit configured to obtain a moving path characteristic parameter by using location information made in a feed by the user through analysis of the personal life log; and a prediction unit configured to predict personality traits by applying the user behavior parameter, the friend relation characteristic parameter and the moving path characteristic parameter to four learned personality traits models.