Mining tourist information from user-supplied collections

Popescu, Adrian; Grefenstette, Gregory; Moëllic, Pierre-Alain

doi:10.1145/1645953.1646211

Cited by 69 publications

(39 citation statements)

References 9 publications

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“…Those photo-sharing services have already led to enormous community-contributed photos with text tags, timestamps and geographic references on the Internet. More importantly, it turns out that the geo-tagged photos can tackle the aforementioned issues in previous methods and provide an effective solution to automatic tourist mobility analysis [10].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu

et al. 2017

IJGI

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Abstract:With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platforms like Flickr have provided a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists and then extends the concept of motifs from topological spaces, to temporal spaces and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.

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Section: Introductionmentioning

confidence: 99%

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu

et al. 2017

IJGI

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“…Extra information can be incorporated within social media, such as geographical information captured by GPS devices. Studying such social media attracts both academic and industrial interests, and it has been a hot research area in recent years [31,11,30,29,3,9,27].…”

Section: Introductionmentioning

confidence: 99%

Diversified Trajectory Pattern Ranking in Geo-Tagged Social Media

Yin¹,

Cao²,

Han³

et al. 2011

Proceedings of the 2011 SIAM International Conference on Data Mining

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Social media such as those residing in the popular photo sharing websites is attracting increasing attention in recent years. As a type of user-generated data, wisdom of the crowd is embedded inside such social media. In particular, millions of users upload to Flickr their photos, many associated with temporal and geographical information. In this paper, we investigate how to rank the trajectory patterns mined from the uploaded photos with geotags and timestamps. The main objective is to reveal the collective wisdom recorded in the seemingly isolated photos and the individual travel sequences reflected by the geo-tagged photos. Instead of focusing on mining frequent trajectory patterns from geo-tagged social media, we put more effort into ranking the mined trajectory patterns and diversifying the ranking results. Through leveraging the relationships among users, locations and trajectories, we rank the trajectory patterns. We then use an exemplar-based algorithm to diversify the results in order to discover the representative trajectory patterns. We have evaluated the proposed framework on 12 different cities using a Flickr dataset and demonstrated its effectiveness.

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“…Los servicios de fotografías compartidas han sido utilizados para identificar los principales puntos de atracción turística y la intensidad de uso de los mismos (Popescu et al, 2009;Kisilevich et al, 2010;Gavric et al, 2011;Straumann et al, 2014 …”

Section: Redes Sociales De Fotografías Geolocalizadasunclassified