Current Challenges and Approaches in Recommending Venues by Using Contextual Suggestion Track from TREC

  • Authors

    • KianLam Tan
    • Haseeb Ur Rehman Khan
    • Chen Kim Lim
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.22049
  • Internet of things (IoT), Contextual Suggestion Track, Recommendation System, Information Retrieval.

  • Abstract

    Contextual suggestion systems have been emerging as an entrance region of research, attributable to the innovative advances in smart connecting things and rapid growth of Big Data. In this regard, the primary purpose of contextual suggestion systems is to propose things that assist users to settle on choices from countless activities, for example, according to their specific context, the system may predict what place users would find interesting to visit or in what restaurant they would prefer to eat. In a smart environment using big data, users’ current activity and past behavior could be incorporated into the suggestion process with an end goal that provides right suggestion at the right time with appropriate location on users personal preferences. The objective of this paper is to provide an overview of contextual suggestion system and a review of TREC’s contextual suggestion track to investigate the approaches have been used in order to develop a model for contextual suggestion.

     

     

  • References

    1. [1] A. Dean-Hall, C. L. A. Clarke, J. Kamps, J. Kiseleva, and E. M. Voorhees. Overview of the TREC 2015 contextual suggestion track. In E. M. Voorhees and A. Ellis, editors, Proceedings of the Twenty-Fourth Text Retrieval Conference (TREC 2015). National Institute for Standards and Technology, NIST Special Publication 500-319, 2016.

      [2] A. Dean-Hall, C. L. A. Clarke, J. Kamps, P. Thomas, and E. M. Voorhees. Overview of the TREC 2012 contextual suggestion track. In E. M. Voorhees and L. P. Buckland, editors, The Twenty-First Text Retrieval Conference Proceedings (TREC 2012). National Institute for Standards and Technology: NIST Special Publication 500-298, 2013.

      [3] A. Dean-Hall, C. L. A. Clarke, J. Kamps, P. Thomas, and E. M. Voorhees. Overview of the TREC 2014 contextual suggestion track. In E.M. Voorhees and A. Ellis, editors, Proceedings of the Twenty-Third Text REtrieval Conference (TREC 2014). National Institute for Standards and Technology, NIST Special Publication 500-308, 2015.

      [4] A. Dean-Hall, C. L. A. Clarke, J. Kamps, P. Thomas, N. Simon, and E. M. Voorhees. Overview of the TREC 2013 contextual suggestion track. In E. M. Voorhees, editor, The Twenty-Second Text Retrieval Conference Proceedings (TREC 2013). National Institute for Standards and Technology. NIST Special Publication 500-302, 2014.

      [5] A. Strehl, J. Ghosh, Cluster ensembles —a knowledge reuse framework for combining multiple partitions, J. Mach. Learn. Res. 3 (2003) 583–617.

      [6] Adeli, H., & Jiang, X. (2009). Intelligent infrastructure: neural networks wavelets, and chaos theory for intelligent transportation systems and smart structures. CRC Press.

      [7] Ben Sassi, I., Mellouli, S. and Ben Yahia, S. (2017). Context-aware recommender systems in mobile environment: On the road of future research. Information Systems, 72, pp.27-61.

      [8] C.D. Manning , P. Raghavan , H. Schütze , Introduction to Information Retrieval, Cambridge University Press, New York, NY, USA, 2008.

      [9] Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65–82.

      [10] Chen, M., Mao, S., & Liu, Y. (2014). Big data: a survey. Mobile Networks and Applications, 19(2), 171–209.

      [11] Chen, Song, Li, & Shen J. (2009). Survey on smart grid technology. Power System Technology, 33(8), 1–7.

      [12] Chourabi, H., Nam, T., Walker, S., Gil Garcia, J. R., Mellouli, S., Nahon, K., & Scholl, H.J. (2012). Understanding smart cities: an integrative framework. Paper presented at the 2012 45th Hawaii International Conference on System Science (HICSS).

      [13] Demirkan, H. (2013). A smart healthcare systems framework. It Professional, 15(5), 38–45.

      [14] Gani, A., Siddiqa, A., Shamshirband, S., &Hanum, F. (2016). A survey on indexing techniques for big data: taxonomy and performance evaluation. Knowledge and Information Systems, 46(2), 241–284.

      [15] I.B. Sassi , S.B. Yahia , S. Mellouli , User-based context modeling for music recommender systems, in: Proceedings of the 23rd International Symposium on Methodologies for Intelligent Systems -Foundations of Intelligent Systems, in: Lecture Notes in Computer Science, 10352, Springer, Warsaw, Poland, 2017, pp. 157–167.

      [16] Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Mahmoud Ali, W. K., Alam, M., &Gani, A. (2014). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014, 8. http://dx.doi.org/10.1155/2014/712826.

      [17] S. H. Hashemi, C. L. A. Clarke, J. Kamps, J. Kiseleva, and E. Voorhees. Overview of the TREC 2016 contextual suggestion track. In Proceedings of TREC 2016, 2016.

      [18] Shadab, B V Babu, (2016), Information Retrieval in Big Data Using Evolutionary Computation: A Survey, International Conference on Computing, Communication and Automation (ICCCA2016) 208-213.

      [19] T. Hussein, T. Linder, W. Gaulke, J. Ziegler. Hybreed: a software framework for developing context-aware hybrid recommender systems, User Model. User-Adapted Interact. 24 (1-2) (2014) 121–174.

      [20] W. Rand, Objective criteria for the evaluation of clustering methods, J. Am. Stat. Assoc. 66 (336) (1971) 846–850.

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  • How to Cite

    Tan, K., Ur Rehman Khan, H., & Kim Lim, C. (2018). Current Challenges and Approaches in Recommending Venues by Using Contextual Suggestion Track from TREC. International Journal of Engineering & Technology, 7(4.19), 207-211. https://doi.org/10.14419/ijet.v7i4.19.22049

    Received date: 2018-11-28

    Accepted date: 2018-11-28

    Published date: 2018-11-27