By a News Reporter-Staff News Editor at Computer Weekly News -- Investigators discuss new findings in Science - Computer Science. According to news originating from Liaoning, People’s Republic of China, by VerticalNews correspondents, research stated, “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.”
Our news journalists obtained a quote from the research from Northeastern University, “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.”
According to the news editors, the research concluded: “Visualized demos and extensive experimental results demonstrate the effectiveness and efficiency of our methods.”
For more information on this research see: Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes. Frontiers of Computer Science , 2017;11(6):1007-1022. Frontiers of Computer Science can be contacted at: Higher Education Press, No 4 Dewai Dajie, Beijing 100120, Peoples R China. (Springer - www.springer.com; Frontiers of Computer Science - http://www.springerlink.com/content/2095-2228/)
The news correspondents report that additional information may be obtained from Y.X. Yu, Northeastern Univ, Sch Comp Sci & Engn, Dept. of Comp Sci, Shenyang 110819, Liaoning, People’s Republic of China. Additional authors for this research include Y.H. Zhao, G. Yu and G.R. Wang.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1007/s11704-016-5501-y. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-01-10), Researchers from Northeastern University Discuss Findings in Computer Science (Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes), Computer Weekly News, 632, ISSN: 1944-1606, BUTTER® ID: 014933978
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