INLP-BPN approach for recommending hotels to a mobile traveler
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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.
KeywordsHotel Mobile Recommendation Integer nonlinear programming Backward propagation network
This study was sponsored by the Ministry of Science and Technology, Taiwan.
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