A hotel recommendation system based on customer location: a link prediction approach

Abstract

Hotel recommendation is one of the most used application areas in recommendation systems. So far, many hotel recommendation systems have been proposed. Most of these systems are based collaborative filtering, content-based filtering, and association rule methods and employ the features of hotel, the ratings given by user, online reviews and comments in social network about the related hotel as data. However, due to the difficulty of processing the data used, the performance rates and speeds of these methods are relatively slow. As a solution to these problems in this paper, we propose a novel hotel recommendation system based on link prediction method. For this purpose, a customer-hotel bipartite network was first constructed and the relationship information in this network was used as data. Then, a supervised link prediction method that consider customers’ location was presented. To the best of our knowledge, this is the first study that recommends hotel by using link prediction method. The experimental results conducted on data crawled from TripAdvisor.com demonstrate that the proposed method captures an accuracy of 89.5% and outperforms the other recent related algorithms.