Abstract
ABSTRACT Many people go sightseeing based on the information available on tourism websites. There is much information available for famous tourist destinations and little information for unknown tourist destinations. In particular, for foreign travelers, it is difficult to find their favorite tourist destinations, so we proposed a personal adaptive tourism recommendation system in former research. During the system development, the method of tourism feature extraction is a key issue. Via a questionnaire, we showed the importance of photo information on tourism websites. As the first step of the tourism feature extraction of photos on tourism websites, we propose two methods of analysis: color analysis and image recognition. Comparing the two methods through experiments, we confirmed that each method had different characteristics and the combination of these methods exhibited the best accuracy in distinguishing between the ratio of artificial and natural objects in the photos.
Highlights
In recent years, the number of people traveling from Japan to foreign countries has significantly increased (JTB tourism research & consulting, 2020)
We proposed a method for extracting general feature words for points of interest (POIs) for Japanese foreign individual travelers (FIT) travelers, using comments from a major tourism website (Li et al, 2019)
For the construction of the POI characteristics analyzing module, we focus on the tourism feature extraction and classification of POI
Summary
The number of people traveling from Japan to foreign countries has significantly increased (JTB tourism research & consulting, 2020). The number of people traveling on foreign independent traveler (FITs) exceeds the number of package tourists (Japanese travel trade news (Official), 2017). Tourism websites play an important role in this trend because they often provide comprehensive and credible information on tourism. Information on these websites is generic, and it is difficult to obtain useful information from tourism websites with high efficiency. Tourism websites and social network services have started to provide various modalities of tourism information, and internet data mining is one of the most common methods of tourism analysis. Compared with social network services providing streamlined information, tourism websites usually gather larger amount of more diverse information, including formal introductions, comments from tourists, and photos
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