Providing Information of Hidden Spot for Tourists to Increase Tourism Satisfaction

Katayama, Shin; Obuchi, Mikio; Okoshi, T.; Nakazawa, Jin

doi:10.1145/3267305.3267647

Cited by 3 publications

(5 citation statements)

References 1 publication

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“…Existing studies on spot mining include studies on clustering spot locations on the Web and SNS [18], and studies on analyzing the attributes of users who post about spots on SNS [13], [43]. Slava et al [18] mine tourist spots by clustering the posted locations of geotagged photos on photo sharing sites using DBSCAN.…”

Section: Tourist Spot Miningmentioning

confidence: 99%

“…Slava et al [18] mine tourist spots by clustering the posted locations of geotagged photos on photo sharing sites using DBSCAN. Katayama et al [13] are mining potential tourist spots from location information in Foursquare check-in logs. Zhuang et al [43] mine spots by classifying the attributes of users who post spot images on Flickr, a photo-sharing website, and by focusing on the composition ratio of local residents and tourists from other regions among users who posted images related to each spot.…”

Section: Tourist Spot Miningmentioning

confidence: 99%

“…The former effort to increase the number of tourist attractions in each area is more important, especially in areas with few well-known tourist attractions. In particular, there are studies [13], [18], [25], [44], [45] on tourism spot mining as a significant approach to increase the number of tourist attractions. These studies on spot mining are based on user-generated content about spots posted on the Web, such as SNS [13], [25] and photo-sharing services [18], [44], [45], to search a tourist destination for obscure spots that are worth visiting but relatively less known.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, there are studies [13], [18], [25], [44], [45] on tourism spot mining as a significant approach to increase the number of tourist attractions. These studies on spot mining are based on user-generated content about spots posted on the Web, such as SNS [13], [25] and photo-sharing services [18], [44], [45], to search a tourist destination for obscure spots that are worth visiting but relatively less known. For example, Katayama et al [13] are mining potential tourist spots from location information in Foursquare check-in logs.…”

Section: Introductionmentioning

confidence: 99%

“…These studies on spot mining are based on user-generated content about spots posted on the Web, such as SNS [13], [25] and photo-sharing services [18], [44], [45], to search a tourist destination for obscure spots that are worth visiting but relatively less known. For example, Katayama et al [13] are mining potential tourist spots from location information in Foursquare check-in logs. Zhuang et al [44] are mining them by clustering geotagged images and comments uploaded on SNS.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Generic POI: A Novel Approach for Discovering Tourist Attractions

Katsumi

Yamada

Ochiai

2023

Journal of Information Processing

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With the rapid spread of social networking services (SNS) and social media, tourism has become a more accessible leisure activity. While many tourists visit well-known tourist destinations, the number of tourists visiting lesser-known ones in non-urban or rural areas has been in decline, and the decline has been exacerbating various problems such as economic disparities. Under these circumstances, it is becoming increasingly important to attract tourists to lesser-known areas with few tourism resources, because attracting tourists brings various positive effects to local residents and municipalities. Existing efforts to attract tourists include studies on tourist spot mining, which search a tourist destination for sightseeing spots that are worth visiting but relatively less well known, based on user-generated content about spots on the Web, such as posts on SNS and images on photo-sharing services. However, as they rely on the amount of user-generated content about potential sightseeing spots to be mined, these methods are limited in discovering a new sightseeing spot that is not recognized as a tourist attraction in advance. To address these challenges, we are inspired by an interesting phenomenon: a previously little-known spot that looks similar to a famous place becomes a hot topic on social media, attracting many people and suddenly becoming a popular tourist attraction. In this paper, we call such a spot with a similar appearance to a well-known one, as generic point-of-interest (POI), and we provide a novel concept of discovering new sightseeing spots by computationally finding generic POIs; we give a new perspective of discovering a new tourist attraction by characterizing a commonplace spot that has a similar appearance to a well-known one as a new tourist attraction. We also propose a pipeline implemented for mining generic POIs, where we calculate the similarity between candidate spots of generic POIs and famous sightseeing spots. Furthermore, we evaluate the feasibility of our approach both through human evaluation and through a photo contest on the Web we held with Tourism Shikoku. The results prove that our proposed method can discover new tourist destinations. The primal advantage of our approach is that it gifts new tourism value to a spot with at least a single place photo similar to a famous sightseeing spot, and thus it discovers a new tourist attraction even in areas with a minimal number of user-submitted images. We hope that our novel concept will be the first step in efforts to attract tourists in various regions around the world, enabling utilization of local spots that even the locals may not be aware of.

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Section: Tourist Spot Miningmentioning

confidence: 99%

Section: Tourist Spot Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterizing Generic POI: A Novel Approach for Discovering Tourist Attractions

Katsumi

Yamada

Ochiai

2023

Journal of Information Processing

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U-KyotoTrip: A Travel Planning System for User Experience Oriented Trips

Yi,

Maekawa,

Kong

et al. 2024

Springer Proceedings in Business and Economics

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We propose U-KyotoTrip, a travel planning system designed for user-experience-oriented trips in Kyoto, Japan. U-KyotoTrip integrates extensive content analysis of user-generated content, Point-Of-Interest (POI) recommendations, and route recommendations, with the aim of assisting users in acquiring information and travel planning. To capture new users’ preferences, we propose user-friendly ask-to-rate methods to handle cold-start user scenarios. Furthermore, we employ five route recommendation methods that vary in their consideration for congestion and tourism diversification to address the overtourism problem. An empirical user study was conducted to evaluate U-KyotoTrip, revealing that users are satisfied with the system, particularly the approach that utilizes photos to present their preferences.

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A Novel Excavation of Fortuitous Tourism Resources Candidates by Analyzing Emotional Information

Ieiri

Hishiyama

2020

Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing

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Providing Information of Hidden Spot for Tourists to Increase Tourism Satisfaction

Cited by 3 publications

References 1 publication

Characterizing Generic POI: A Novel Approach for Discovering Tourist Attractions

Characterizing Generic POI: A Novel Approach for Discovering Tourist Attractions

U-KyotoTrip: A Travel Planning System for User Experience Oriented Trips

A Novel Excavation of Fortuitous Tourism Resources Candidates by Analyzing Emotional Information

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