Skip to main content
SpringerLink
Log in

INLP-BPN approach for recommending hotels to a mobile traveler

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Existing systems for recommending hotels to mobile travelers are subject to several problems. For example, a traveler might choose a dominated hotel that is inferior to another hotel in all aspects. This problem cannot be solved by simply changing the weights assigned to the attributes of a hotel. In addition, a nonlinear recommendation mechanism, instead of a linear one, may be more effective for tailoring the recommendation result to a traveler’s choice. To address these concerns, this study applied two treatments. First, an artificial attribute is added to each hotel to model a traveler’s unknown preference for that hotel. The value of a traveler’s unknown preference is determined by solving an integer nonlinear programming problem. Subsequently, a backward propagation network is constructed to map the recommendation results to travelers’ choices, to improve the successful recommendation rate. The effectiveness of the proposed methodology was evaluated in a field study conducted in a small region of Seatwen District, Taichung City, Taiwan, and the experimental results supported its superiority over several existing methods in improving the successful recommendation rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Changchien SW, Huang RH (2003) Recognizing customer knowledge level towards products for recommendation in electronic commerce. In: Proceedings of the international conference on electronic business, pp 217–219

  • Chen T (2015a) Enhancing the performance of a ubiquitous location-aware service system using a fuzzy collaborative problem solving strategy. Comput Ind Eng 87:296–307

    Article  Google Scholar 

  • Chen T (2015b) Ubiquitous hotel recommendation using a fuzzy-weighted-average and back-propagation-network approach. Int J Intell Syst (in press)

  • Chen T (2016) Ubiquitous multicriteria clinic recommendation system. J Med Syst 40:113. doi:10.1007/s10916-016-0469-6

    Article  Google Scholar 

  • Chen T, Chuang YH (2015) Fuzzy and nonlinear programming approach for optimizing the performance of ubiquitous hotel recommendation. J Ambient Intell Humaniz Comput. doi:10.1007/s12652-015-0335-2

    Google Scholar 

  • Chen T, Honda K (2015) Solving data preprocessing problems in existing location-aware systems. J Ambient Intell Humaniz Comput. doi:10.1007/s12652-015-0330-7

    Google Scholar 

  • Chen Z, Xia JC, Irawan B, Caulfied C (2014) Development of location-based services for recommending departure stations to park and ride users. Transp Res Part C 48:256–268

    Article  Google Scholar 

  • De Maio C, Fenza G, Gaeta M, Loia V, Orciuoli F, Senatore S (2012) RSS-based e-learning recommendations exploiting fuzzy FCA for Knowledge Modeling. Appl Soft Comput 12(1):113–124

    Article  Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    MATH  Google Scholar 

  • Ducatel K, Bogdanowicz M, Scapolo F, Leijten J, Burgelman J-C (2001) Scenarios for ambient intelligence in 2010. IST Advisory Group Final Report, European Commission, Brussels

  • Fenza G, Fischetti E, Furno D, Loia V (2011) A hybrid context aware system for tourist guidance based on collaborative filtering. In: IEEE international conference on fuzzy systems, pp 131–138

  • Firoze A, Arifin MS, Rahman RM (2013) Bangla user adaptive word Speech recognition: approaches and comparisons. Int J Fuzzy Syst Appl 3(3):1–36

    Article  Google Scholar 

  • Krevl A, Ciglarič M (2006) A framework for developing distributed location based applications. In: 20th international parallel and distributed processing symposium

  • Lin KP, Lai CY, Chen PC, Hwang SY (2015) Personalized hotel recommendation using text mining and mobile browsing tracking. In: IEEE international conference on systems, man, and cybernetics, pp 191–196

  • Ranganathan A (2014) The Levenberg–Marquardt algorithm. http://www.scribd.com/doc/10093320/Levenberg-Marquardt-Algorithm. Accessed 6 Sept 2016

  • Wu Z, Palmer M (1994) Verb semantics and lexical selection. In: Proceedings of the 32nd annual meeting of the association for computational linguistics

  • Zhang K, Wang K, Wang X, Jin C, Zhou A (2015) Hotel recommendation based on user preference analysis. In: 31st IEEE international conference on data engineering workshops, pp 134–138

Download references

Acknowledgments

This study was sponsored by the Ministry of Science and Technology, Taiwan.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Systems Management, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung, Taiwan

    Toly Chen & Chi-Wei Lin

Authors
  1. Toly Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chi-Wei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chi-Wei Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, T., Lin, CW. INLP-BPN approach for recommending hotels to a mobile traveler. J Ambient Intell Human Comput 9, 329–336 (2018). https://doi.org/10.1007/s12652-016-0407-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-016-0407-y

Keywords

Search

Navigation