RankTagViz: A Semantic Ranking and Tags Visualization of User Travelogues

  • Authors

    • M. Uma Maheswari
    • J.G.R. Sathiaseelan
    2018-05-01
    https://doi.org/10.14419/ijet.v7i2.22.12304
  • Travelogues, Semantic, Ranking, Travel Tags, Visualization

  • Abstract

    The travelogues contain prosperous geo-referenced information such as tours, weather, and expenses etc. Reading travelogues and finding useful trip information is a tedious task for many people. So as to correlate the georeferenced text meaningfully the semantic ranking is taken in account on the user contributing travelogues. In this way there is a requirement for creating programmed travelogue mining methods to pass on valuable data in a travelogue to its peruses in the more successful way. Visualizing a geo-referenced data with location tags and descriptions makes it helpful for readers to understand the fundamental substance of the travelogue. For user expedient, this paper proposes a Travelogue RankTagViz approach that semantically ranks and visualizes the tags of user travelogues. The RankTagViz contains two phases. In the first phase, the user travelogues are ranked based on semantic. In the next phase, the travelogues are visualized based on the tag and images. During semantic ranking, a semantic dimension reduction method is proposed to pre-process and mine useful information from the travelogues. After that a semantic rank mechanism is proposed to rank travelogues based on tags and POI (Point Of Interest). For tag visualization, location based tags and images are extracted from the travelogues and a novel UI is intended to give a superior client encounter, by arranging both the literary and visual data produced by committed voyagers in an alluring way. Test comes about on an arrangement of gathered travelogues show the proposed techniques' capacity to rank and imagine travelogues.

  • References

    1. [1] Zheng, Y. T., Zha, Z. J., & Chua, T. S. (2012). "Mining travel patterns from geotagged photos". ACM Transactions on Intelligent Systems and Technology, 3(3), 1-18

      [2] G. Cai, C. Hio, L. Bermingham, L. Kyungmi and I. Lee. (2014). "Mining Frequent Trajectory Patterns and Regions-of-Interest from Flickr Photos" . In Proc. of HICSS, 2014

      [3] Yi-Hau Liu, Shian-Hua Lin, Chun-Ku Lai, Chun-Che Huang, Cheng-Yu Lu. (2016). "Mining Crowdsourcing Photos for Recognizing Landmark Areas", IMIS 2016: 12-19

      [4] Q. Hao, R. Cai, X.-J.Wang, J.-M. Yang, Y. Pang, and L. Zhang.(2009). "Generating location overviews with images and tags by mining user-generated travelogues". In Proc. ACM Multi-media, 2009

      [5] Q. Hao, R. Cai , C. Wang , R. Xiao , J.-M. Yang, Y. Pang, L. Zhang (2010). "Equip tourists with knowledge mined from travelogues". In: Proceedings of the 19th international conference on World wide web.ACM, pp 401–410

      [6] Q. Hao, R. Cai, J. M. Yang et al. (2009). "TravelScope: standing on the shoulders of dedicated travelers," in Proceedings of the 17th ACM International Conference on

      [7] Multimedia, MM’09, with Co-located Workshops and Symposiums, pp. 1021–1022, October 2009.

      [8] M. Ye, R. Xiao, W. C. Lee, and X. Xie, (2011). "On theme location discovery for travelogue services," in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, pp. 465–474, 2011

      [9] T. Kurashima, T. Tezuka, and K. Tanaka,(2006). "Mining and visualizing local experiences from blog entries," in Database and Expert Systems Applications. Springer, 2006, pp. 213–222.

      [10] Y. Gao et al. (2010). "W2go: a travel guidance system by automatic landmark ranking". In ACM MM, 2010.

      [11] Y. Du, Y. Hai. (2013)."Semantic ranking of web pages based on formal concept analysis" Journal of Systems and Software. 2013; 86(1): 187-97.

      [12] A. Ghose, P.G. Ipeirotis, and L. Beibei, (2012). "Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content", Marketing Science, 31, 3, 493-520.

      [13] D. Liu, X.-S. Hua, L. Yang, M. Wang, and H.-J. Zhang,(2009). "Tag ranking," in Proc. ACM Conf. World Wide Web, 2009, pp. 351–360.

      [14] M. Fan, Q. Zhou, and T. F. Zheng,(2012). "Content-based semantic tag ranking for recommendation," in WI-IAT, 2012

      [15] Y. Xue, , X.Qian. (2012) "Visual summarisation of Landmarks via viewpoint modeling", Proc, ICIP, 2012, pp. 2873–2876

      [16] Y. Ren,X.Qian,andS.Jiang, (2015) "Visual Summarization for Place-of-Interest by Social Contextual Constrained Geo-clustering," in Proc. MMSP’15, 2015.

      [17] S. Rudinac, A. Hanjalic, M.A. Larson, (2013) "Generating Visual Summaries of Geographic Areas Using Community - Contributed Images", IEEE Transactions on Multimedia, 15(4), pp. 921-932, 2013.

      [18] X. Lu, Y. Pang Q. Hao and L. Zhang, (2009)."Visualizing textual travelogue with location-relevant images. In Proceedings of the 2009 International Workshop on Location Based Social Networks(2009), ACM, pp. 65–68

      [19] O. Kucuktunc, S.G. Sevil, A. Tosun, H. Zitouni, P. Duygulu, and F. Can. (2008) "Tag suggestr: Automatic photo tag expansion using visual information for photo sharing websites". In Proc. Semantic and Digital Media Technologies: Semantic Multimedia , pages 61–73, 2008.

      [20] Y. Pang, Q. Hao, Y. Yuan, T. Hu, R. Cai, and L. Zhang, (2011), "Summarizing tourist destinations by mining user-generated travelogues and photos," Comput. Vis. Image Understand. , vol. 115, no. 3, pp. 352–363, Mar. 2011.

      [21] https://www.indiatravelogue.com/

      [22] https://www.holidify.com/

      [23] https://www.inditales.com/

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  • How to Cite

    Maheswari, M. U., & Sathiaseelan, J. (2018). RankTagViz: A Semantic Ranking and Tags Visualization of User Travelogues. International Journal of Engineering & Technology, 7(2.22), 53-56. https://doi.org/10.14419/ijet.v7i2.22.12304

    Received date: 2018-05-01

    Accepted date: 2018-05-01

    Published date: 2018-05-01