## 5 Conclusions and Future Works  
Location Based Service (LBS) and recommendation system are typical examples of personalized service. For example, contents providers such as Netflix opened a competition for recommendation system development. However, recommendation systems have cold start problem which makes recommendation difficult for the new users or new contents. In addition, personal information protection of history is another sort of problem. It could be possible if we can predict user preference based on basic user features regardless of history. From various research, it is discussed that human personality and location preference is highly related. Additionally, personal features other than personality also related with preference of location visiting. Of course, there are meaningful, distinguished personal features for personal location preference.  
In this research, from three different methods of machine learning, we figured out the effects of distinguished personal features for personal location preference. As a result, eight location categories out of ten showed meaningful prediction accuracy: Retail Business, Service industry, Restaurant, Pub, Beverage Store, Theater and Concert Hall, Institutions of Education, and Museum, Gallery, Historical sites, Tourist spots. For each of three algorithms, prediction accuracy seem to be reliable with very similar tendency  
Figure 6: Predict Accuracy Comparison of all methods.  
of analysis results. As well, input features were selected which affects location category selection with Random Forest and XGBoost, and of course, input features are dependent on location categories. Based on our research, visiting preference to such location categories can be highly predictable from personal features. In addition, hyperparameter optimization which is a sort of AutoML technology is introduced in order to increase prediction accuracy. Grid search, random search as well as bayesian optimization are applied and the results are compared.  
In our research, we demonstrated a method for visiting place prediction. For large amount of input data, feature selection is applied in order to reduce dimension of data and increase quality of input data. In such cases, Stacking could be one of the best solution even without hyperparameter optimization. On the contrary, for smaller number of input data, bagging or boosting in addition with hyperparameter optimization could be a better solution since Stacking may show poor prediction accuracy. We need to research further, especially for location categories such as service industry, retail business since too many subcategories make the categorization vague. In addition, less sensitive personal features must exists for the prediction of visiting location. Such features will be able to be identified. From the aspect of volunteers' data, we need to expand data collection for more number of data as well as wider span of data, since our data is clear limitations of volunteer pool; engineering students in his or her twenties.  
Conceptualization, H.Y.S.; methodology, Y.M.K.; software, Y.M.K.; validation, H.Y.S.; formal analysis, Y.M.K.; investigation, H.Y.S.; resources, H.Y.S.; data curation, Y.M.K.; writing--original draft preparation, Y.M.K.; writing--review and editing, H.Y.S.; visualization, Y.M.K.; supervision, H.Y.S.; project administration, H.Y.S.; funding acquisition, H.Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (NRF-2019R1F1A1056123).  
The authors declare no conflict of interest.