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Proceedings ArticleDOI

Photo2Trip: generating travel routes from geo-tagged photos for trip planning

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TLDR
This paper proposes to leverage existing travel clues recovered from 20 million geo-tagged photos collected from www.panoramio.com to suggest customized travel route plans according to users' preferences, and can provide a customized trip plan for a tourist.
Abstract
Travel route planning is an important step for a tourist to prepare his/her trip. As a common scenario, a tourist usually asks the following questions when he/she is planning his/her trip in an unfamiliar place: 1) Are there any travel route suggestions for a one-day or three-day trip in Beijing? 2) What is the most popular travel path within the Forbidden City? To facilitate a tourist's trip planning, in this paper, we target at solving the problem of automatic travel route planning. We propose to leverage existing travel clues recovered from 20 million geo-tagged photos collected from www.panoramio.com to suggest customized travel route plans according to users' preferences. As the footprints of tourists at memorable destinations, the geo-tagged photos could be naturally used to discover the travel paths within a destination (attractions/landmarks) and travel routes between destinations. Based on the information discovered from geo-tagged photos, we can provide a customized trip plan for a tourist, i.e., the popular destinations to visit, the visiting order of destinations, the time arrangement in each destination, and the typical travel path within each destination. Users are also enabled to specify personal preference such as visiting location, visiting time/season, travel duration, and destination style in an interactive manner to guide the system. Owning to 20 million geo-tagged photos and 200,000 travelogues, an online system has been developed to help users plan travel routes for over 30,000 attractions/landmarks in more than 100 countries and territories. Experimental results show the intelligence and effectiveness of the proposed framework.

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Journal ArticleDOI

Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

TL;DR: A comprehensive introduction to a large body of research, more than 200 key references, is provided, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix.
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Big data in tourism research: A literature review

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BookDOI

Computing with Spatial Trajectories

Yu Zheng, +1 more
TL;DR: This book presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks.
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Recommendations in location-based social networks: a survey

TL;DR: A panorama of the recommender systems in location-based social networks with a balanced depth is presented, facilitating research into this important research theme.
Journal ArticleDOI

Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS

TL;DR: In this paper, the authors demonstrate the potential of photo-sharing services for identifying and analyzing the main tourist attractions in eight major European cities: Athens, Barcelona, Berlin, London, Madrid, Paris, Rome and Rotterdam.
References
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Journal ArticleDOI

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Proceedings ArticleDOI

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Proceedings ArticleDOI

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Proceedings ArticleDOI

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