A novel hotel recommendation method based on personalized preferences and implicit relationships / Ke Chen, Peng Wang, and Hong-yu Zhang.

Includes bibliographical references (pages 11-12).

On tourism websites, hotel recommendations have drawn growing attention from researchers, as they can help customers select a satisfactory hotel from many options with massive information. However, some inherent challenges exist in conventional hotel recommendations, specifically the extent to which there is considerable room for improvement in user preference models and neighbour recognition. Therefore, we propose a two-stage hotel recommendation approach that employs hotel feature information to support preference analysis. First, in the filling stage, association rules between features are considered to accurately capture users’ personalized preferences, which can be incorporated with public preferences to estimate potential ratings of users for unvisited hotels. Then, in the recommendation stage, we combine rating similarities between users with their closeness relationships to identify more reliable neighbours. Finally, a hotel recommendation case on Ctrip.com is performed to evaluate the model. Experimental results confirm that our method outperforms the other five benchmark methods.