Integer nonlinear programming and optimized weighted-average approach for mobile hotel recommendation by considering travelers’ unknown preferences

Yu-Cheng Lin (), Toly Chen () and Li-Chih Wang
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Yu-Cheng Lin: Overseas Chinese University
Toly Chen: National Chiao Tung University
Li-Chih Wang: Tunghai University

Operational Research, 2018, vol. 18, issue 3, No 4, 625-643

Abstract: 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.

Keywords: Hotel; Recommendation; Integer nonlinear programming; Dominated solution (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s12351-016-0270-9

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