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Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes

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Abstract

Instagram is a popular photo-sharing social application. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of geo-tagged photos with spatio-temporal information are generated along tourist’s travel trajectories. Such Instagram photo trajectories consist of travel paths, travel density distributions, and traveller behaviors, preferences, and mobility patterns. Mining Instagram photo trajectories is thus very useful for many mobile and location-based social applications, including tour guide and recommender systems. However, we have not found any work that extracts interesting group-like travel trajectories from Instagram photos asynchronously taken by different tourists. Motivated by this, we propose a novel concept: coterie, which reveals representative travel trajectory patterns hidden in Instagram photos taken by users at shared locations and paths. Our work includes the discovery of (1) coteries, (2) closed coteries, and (3) the recommendation of popular travel routes based on closed coteries. For this, we first build a statistically reliable trajectory database from Instagram geo-tagged photos. These trajectories are then clustered by the DBSCAN method to find tourist density. Next, we transform each raw spatio-temporal trajectory into a sequence of clusters. All discriminative closed coteries are further identified by a Cluster-Growth algorithm. Finally, distance-aware and conformityaware recommendation strategies are applied on closed coteries to recommend popular tour routes. Visualized demos and extensive experimental results demonstrate the effectiveness and efficiency of our methods.

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Authors and Affiliations

  1. Department of Computer Science, School of Computer Science & Engineering, Northeastern University, Shenyang, 110819, China

    Yaxin Yu, Yuhai Zhao, Ge Yu & Guoren Wang

Authors
  1. Yaxin Yu
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  2. Yuhai Zhao
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  3. Ge Yu
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  4. Guoren Wang
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Corresponding author

Correspondence to Yaxin Yu.

Additional information

Yaxin Yu received her BE in mechanics, ME and PhD in computer science, from Northeastern University (NEU), China in 1993, 2000, and 2004, respectively. Currently she is an associate professor in the School of Computer Science & Engineering, NEU. She is a member of IEEE ACM, and a member of CCF. Her major research interests include data mining and social networks.

Yuhai Zhao received his BE, ME, and PhD in computer science from Northeastern University (NEU), China in 1999, 2004 and 2007, respectively. Currently he is a professor in the School of Computer Science & Engineering, NEU. He is a member of IEEE ACM, and a member of CCF. His major research interests include data mining and bioinformatics.

Ge Yu received his PhD degree in computer science from Kyushu University, Japan in 1996. Currently he is a professor and PhD supervisor in the School of Computer Science & Engineering, Northeastern University, China. He is a senior member of IEEE ACM, and a senior member of CCF. His major research interests include database theory and technology, parallel computing and cloud computing, etc.

Guoren Wang received his BE, ME, and PhD in computer science from Northeastern University (NEU), China in 1988, 1991, and 1996, respectively. Currently he is a professor and PhD supervisor in the School of Computer Science & Engineering, NEU. He is a senior member of IEEE ACM, and a senior member of CCF. His major research interests include big data, bioinformatics and image processing, etc.

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Yu, Y., Zhao, Y., Yu, G. et al. Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes. Front. Comput. Sci. 11, 1007–1022 (2017). https://doi.org/10.1007/s11704-016-5501-y

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  • DOI: https://doi.org/10.1007/s11704-016-5501-y

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