Mining tourist motive for marketing development via twice-learning

Abstract

Prediction of tourist decision-making processes, including tourist motive, attitude, behavior, and so on, is of great importance for the development of tourism marketing strategies. Recently, applying machine learning techniques to predict tourist decision-making processes has drawn much attention. However, many machine learning techniques applied in the tourist decision-making prediction task fail to address two practical yet important problems. One is the failure of constructing models that can generate accurate yet comprehensible predictions at the same time, and the other is the failure to accommodate the characteristics of data collected from tourists that is usually small yet noise prone. In this article, we address the two entangled problems using the twice-learning framework to predict tourist motive from tourist external and internal features data collected through on-site survey. The results indicate that, based on the two-phase learning process, we can predict tourist motive accurately as well as extract meaningful insights, which are useful for targeted marketing strategies development from the real-world data. © 2015