TRSO: A Tourism Recommender System Based on Ontology

Abstract

In the era of information explosion, the Internet has become one of the most important tools for users to get information. As one of the main applications, most of the tourists, if not all, utilize the search engine to obtain the useful travelling information online which makes tourism recommender systems valuable. However, given a huge amount of online information, it still remains challenging to develop an effective tourism recommender system. To tackle this challenge, in this work, we propose TRSO, an ontology-based tourism recommender system by incorporating different techniques. First, we adopt the association rules to dig out the associated users from a large number of users. By doing so, users in the database are divided into two categories: related users and unrelated users. Second, for the related users, we propose a collaborative filtering algorithm by incorporating the time and evaluation factors. For the unrelated users, we utilize a different collaborative filtering algorithm, which integrates the time factor and the tourism attraction ontology information. Third, we further filter useless information according to the context information. Finally, we expand the tourism attraction with other tourism information such as shopping, eating and traveling based on a tourism ontology. The experimental results on the standard benchmark show that the proposed tourism recommendation algorithm can achieve satisfactory and comprehensive recommendation performance.