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Travel topic analysis: a mutually reinforcing method for geo-tagged photos


Sharing personal activities on social networks is very popular nowadays, where the activities include updating status, uploading dining photos, sharing video clips, etc. Finding travel interests hidden in these vast social activities is an interesting but challenging problem. In this work, we attempt to discover travel interests based on the spatial and temporal information of geo-tagged photos. Obviously the visit sequence of a traveler can be approximately captured by her shared photos based on the timestamps and geo-locations. To extract underlying travel topics from abundant visit sequences, we study a novel mixture model to estimate the visiting probability of regions of attractions (ROAs). Such travel topics can be used in different applications, such as advertisements, promotion strategies, and city planning. To enhance the estimation result, we propose a mutual reinforcement framework to improve the quality of ROAs. Finally, we thoroughly evaluate and demonstrate our findings by the photo sharing activities collected from Flickr TM.

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    It should be noted that all trajectories (including t 1,t 2, and t 5) are taken into consideration. This is intuitive to the real world scenario, where a traveler may have multiple travel interests so that her tour can be partitioned into several travel topics.

  2. 2.

    To simplify our evaluation, we use minimum bounded rectangle to assign the photos into the ROA of \(\mathbb {M}\).

  3. 3.

    Yang et al. [40] is a parameter free technique.

  4. 4.

    This is because the travel topics discovered by PLSA do not necessarily follow the Dirichlet distribution.


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This project was supported by grants SRG007-FST11-LHU, MYRG188-FST11-GZG, MYRG109(Y1-L3)-FST12-ULH from University of Macau RC and grant FDCT/106/2012/A3 from Macau FDCT.

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Correspondence to Leong Hou U.

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Kou, N.M., U, L.H., Yang, Y. et al. Travel topic analysis: a mutually reinforcing method for geo-tagged photos. Geoinformatica 19, 693–721 (2015).

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  • Web images
  • Travel analysis
  • Regions of attraction
  • Mixture models