Finding location visiting preference from personal features with ensemble machine learning techniques and hyperparameter optimization

초록

For the question regarding the relationship between personal factors and location selection, many researches support the effect of personal features for personal location favorite. However, it is also found that not all of personal factors are effective for location selection. In this research, only distinguished personal features excluding meaningless features are used in order to predict visiting ratio of specific location categories by using three different machine learning techniques: Random Forest, XGBoost, and Stacking. Through our research, the accuracy of prediction of visiting ratio to a specific location regarding personal features are analyzed. Personal features and visited location data had been collected by tens of volunteers for this research. Different machine learning methods showed very similar tendency in prediction accuracy. As well, precision of prediction is improved by application of hyperparameter optimization which is a part of AutoML. Applications such as location based service can utilize our result in a way of location recommendation and so on. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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