Cultural difference and visual information on hotel rating prediction

Due to the emergence of hotel social media platforms, how to discover interesting properties and utilize these discovered characteristics in hotel-related applications become important issues. In this work, we extend a large-scale hotel information collection to include heterogeneous hotel information, in order to facilitate multimodal and cross-culture analysis. With this rich dataset, we analyze various correlations between hotel properties and unveil interesting characteristics that would benefit hotel recommendation. We found that travelers from different cultural areas (countries) have different rating behaviors. In addition, beyond the scope of conventional text-based hotel analysis, we utilize visual analysis techniques to analyze hotel’s cover photo, and investigate the relationship between rating behaviors and visual information. We adopt these correlations to predict hotel ratings, and verify that by considering visual information and cultural difference, prediction performance can be improved.