Choosing a tourist destination from the information that is available on the Internet and through other sources is one of the most complex tasks for tourists when planning travel, both before and during travel. Previous Travel Recommendation Systems (TRSs) have attempted to solve this problem. However, some of the technical aspects such as system accuracy and the practical aspects such as usability and satisfaction have been neglected. To address this issue, it requires a full understanding of the tourists' decision-making and novel models for their information search process. This paper proposes a novel human-centric TRS that recommends destinations to tourists in an unfamiliar city. It considers both technical and practical aspects using a real world data set we collected. The system is developed using a two-steps feature selection method to reduce number of inputs to the system and recommendations are provided by decision tree C4.5. The experimental results show that the proposed TRS can provide personalized recommendation on tourist destinations that satisfy the tourists.
|Title of host publication||2015 21st International Conference on Automation and Computing|
|Subtitle of host publication||Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015|
|Publication status||Published - 2 Nov 2015|
|Event||21st International Conference on Automation and Computing, ICAC 2015 - Glasgow, United Kingdom|
Duration: 11 Sep 2015 → 12 Sep 2015
|Conference||21st International Conference on Automation and Computing, ICAC 2015|
|Period||11/09/15 → 12/09/15|