Abstract
The existing mobile hotel recommendation systems are usually subject to a difficult problem—travelers choose dominated hotels. This problem is difficult to resolve because there is no reason to recommend a hotel that is inferior to another in all aspects. To address this problem, an artificial dimension is added to each hotel to model unknown personal preferences. The possible values along the artificial dimension and the weight associated with it are derived by solving an integer nonlinear programming problem. Thus, the proposed methodology hybridizes objective and subjective weights. An illustrative example is provided to show the applicability of the proposed methodology. In addition, a field study was conducted in a small region of Seatwen District, Taichung City, Taiwan to evaluate the possible advantages of the proposed methodology over existing methods. The experimental results showed that the proposed methodology outperformed five existing methods in improving the successful recommendation rate, with the most significant advantage being up to 33 %. Furthermore, the recommendation results generated using the proposed methodology were found to be less risky.
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References
Chang PT, Hung KC (2005) Applying the fuzzy-weighted-average approach to evaluate network security systems. Comput Math Appl 49(11):1797–1814
Chen T (2015a) An efficient and effective fuzzy collaborative intelligence approach for cycle time estimation in wafer fabrication. Int J Intell Syst 30:620–650
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 (in press)
Chen T, Chuang YH (2015) Fuzzy and nonlinear programming approach for optimizing the performance of ubiquitous hotel recommendation. J Ambient Intell Humaniz Comput (in press)
Chen T, Honda K (2015) Solving data preprocessing problems in existing location-aware systems. J Ambient Intell Humaniz Comput (in press)
Chen Y, Fung RY, Tang J (2006) Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. Eur J Oper Res 174(3):1553–1566
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
Ding JF (2012) Using modified fuzzy perception-importance analysis to evaluate customer value for global shipping carrier-based logistics service providers. Inf Technol J 11(10):1434
Garbers J, Niemann M, Mochol M (2006) A personalized hotel selection engine. The 3rd European semantic web conference, pp 1–2
Gobin BA, Subramanian RK (2007) Knowledge modeling for a hotel recommendation system. World Acad Sci Eng Technol 25:244–248
Huming G, Weili L (2010) A hotel recommendation system based on collaborative filtering and rankboost algorithm. 2010 Second international conference on multimedia and information technology, pp 317–320
Kowatsch T, Maass W (2010) In-store consumer behavior: How mobile recommendation agents influence usage intentions, product purchases, and store preferences. Comput Hum Behav 26(4):697–704
Lei K, Yu Q, Ning R (2013) A device-similarity-based recommendation system in mobile terminals. Fourth international conference on networking and distributed computing, pp 60–64
Levi A, Mokryn O, Diot C, Taft (2012) Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 115–122
Rinner C, Raubal M (2004) Personalized multi-criteria decision strategies in location based decision support. J Geogr Inf Sci 10:149–156
Tsai H-R, Chen T (2014) Enhancing the sustainability of a location-aware service through optimization. Sustainability 6:9441–9455
Wu H-C, Chen T (2015) CART–BPN approach for estimating cycle time in wafer fabrication. J Ambient Intell Humaniz Comput 6:57–67
Xiong Y, Geng L (2010) Personalized intelligent hotel recommendation system for online reservation—a perspective of product and user characteristics. International conference on management and service science, pp 1–5
Yin C, Zhang H, Xiang J, Yin Z, Wang J (2015) A new mobile recommendation algorithm based on statistical theory. 4th International conference on advanced information technology and sensor application, pp 96–99
Yu C-C, Chang H-P (2009) Personalized location-based recommendation services for tour planning in mobile tourism applications. Lect Notes Comput Sci 5692:38–49
Zheng VW, Cao B, Zheng Y, Xie X, Yang Q (2010) Collaborative filtering meets mobile recommendation: a user-centered approach. Twenty-Fourth AAAI Conf Artif Intell 10:236–241
Zheng VW, Zheng Y, Xie X, Yang Q (2012) Towards mobile intelligence: learning from GPS history data for collaborative recommendation. Artif Intell 184:17–37
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This study was sponsored by the Ministry of Science and Technology, Taiwan.
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Lin, YC., Chen, T. & Wang, LC. Integer nonlinear programming and optimized weighted-average approach for mobile hotel recommendation by considering travelers’ unknown preferences. Oper Res Int J 18, 625–643 (2018). https://doi.org/10.1007/s12351-016-0270-9
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DOI: https://doi.org/10.1007/s12351-016-0270-9