A fuzzy ubiquitous traveler clustering and hotel recommendation system by differentiating travelers’ decision-making behaviors

For generating hotel recommendations, clustering travelers has been demonstrated to be a viable method to elevate traveler satisfaction with the recommendation results. However, most of the existing methods that adopt this approach cluster travelers according to a variety of traveler or hotel attributes, which may not necessarily be appropriate for use in an online application such as ubiquitous hotel recommendation. To overcome this problem, a fuzzy ubiquitous traveler clustering and hotel recommendation (FUTCHR) system was developed in this study. The FUTCHR system clustered travelers according to their decision-making mechanisms that are fitted by comparing travelers’ choices with the recommendation results in the historical data. To generate recommendations, a fuzzy mixed binary-nonlinear programming model was constructed and solved. The novelty of the proposed methodology is to cluster travelers without knowing their characteristics but according to the differences in their decision-making mechanisms. The FUTCHR system was employed in a regional study, and the successful recommendation rate was superior to three existing methods in this field.