An Artificial Neural Network-based technique for on-line hotel booking

In this paper the use of Artificial Neural Networks (ANNs) in on-line booking for hotel industry is investigated. The paper details the description, the modeling and the resolution technique of on-line booking. The latter problem is modeled using the paradigms of machine learning, in place of standard ‘If-Then-Else’ chains of conditional rules. In particular, a supervised three layers MultiLayer Perceptron (MLP) ANN is adopted, which is trained using information from previous customers’ reservations. Performances of our ANNs are analyzed: they behave in a quite satisfactory way in managing the (simulated) booking service in a hotel. The customer requires single or double rooms, while the system gives as a reply the confirmation of the required services, if available. Moreover, in the case rooms or services are not at disposal, we highlight that using our approach the system proposes alternative accommodations (from two days in advance to two days later with respect to the requested day). Numerical results are given, where the effectiveness of the proposed approach is critically analyzed. Finally, we outline guidelines for future research.

In this paper the use of Artificial Neural Networks (ANNs) in on-line booking for hotel industry is investigated. The paper details the description, the modeling and the resolution technique of on-line booking. The latter problem is modeled using the paradigms of machine learning, in place of standard 'If-Then-Else' chains of conditional rules. In particular, a supervised three layers MultiLayer Perceptron (MLP) ANN is adopted, which is trained using information from previous customers' reservations. Performances of our ANNs are analyzed: they behave in a quite satisfactory way in managing the (simulated) booking service in a hotel. The customer requires single or double rooms, while the system gives as a reply the confirmation of the required services, if available. Moreover, in the case rooms or services are not at disposal, we highlight that using our approach the system proposes alternative accommodations (from two days in advance to two days later with respect to the requested day). Numerical results are given, where the effectiveness of the proposed approach is critically analyzed. Finally, we outline guidelines for future research. (C) 2014 The Authors. Published by Elsevier B. V.