Abstract: Tourism recommender systems have been widely used in our daily life to recommend tourist spots to users meeting their preference. In this paper, we propose a contentbased tourism recommender system considering travel season of users. In order to characterize seasonal variable features of spots,the proposed system generates seasonal feature vectors in three steps: 1) to identify the vocabulary concerned through Wikipedia; 2) to identify the trend over all spots through Twitter for each season; and 3) to highlight the weight of words contained in each identified trend. In the decision of recommendation, it does not only match the user profile with features of spots but also takes user’s travel season into account. The effectiveness of the proposed system is evaluated by a series of experiments, i.e.computer simulation and questionnaire evaluation. The result indicates that: 1) those vectors certainly reflect the similarity of spots for designated time period, and 2) with using such vectors of spots, the system successfully realized a tourism seasonal recommendation.

Guan-Shen Fang, Sayaka Kamei and Satoshi Fujita. “A Japanese Tourism Recommender System with Automatic Generation of Seasonal Feature Vectors”. International Journal of Advanced Computer Science and Applications (IJACSA) 8.6 (2017). http://dx.doi.org/10.14569/IJACSA.2017.080645

@article{Fang2017,
title = {A Japanese Tourism Recommender System with Automatic Generation of Seasonal Feature Vectors},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080645},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080645},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {6},
author = {Guan-Shen Fang and Sayaka Kamei and Satoshi Fujita}
}