Research on Hotel Customer Relationship Management System Based on the Classification Algorithm

Research on Hotel Customer Relationship Management System Based on the Classification Algorithm

Zhao Weili (Changchun Vocational Institute of Technology, Changchun City, China)
DOI: 10.4018/IJISSCM.2019040105
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The hotel management relationship is a good business strategy for hotels, which can promote the development of a hotel, when a classification algorithm is applied to customer relationship management system. First, the classification algorithm is based on a support vector machine is studied, the nearest neighbor sample density is used, and the corresponding mathematical model is constructed. Second, the procedure of a classification algorithm based on fuzzy support vector machine is designed. Third, a customer acquisition plan based on a classification algorithm is analyzed. Finally, a hotel is used as the research object, and a customer acquisition analysis is carried out, and the results show that the new method has quicker training speed and higher classification correctness.
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Classification Algorithm Based On Support Vector Machine

Classification algorithm is a kind of data mining technology, which can construct a classification function or model based on characteristics of data collection. Currently, classification algorithm concludes decision tree method, Bayesian method, support vector machine, genetic algorithm and so on. The support vector machine has good generalization ability, and has good nonlinear data processing ability, which has been applied in many fields, such as fault diagnosis, image processing, and text categorization. Therefore, the data mining classification algorithm base on fuzzy support vector machine is applied in customer relationship management (Mo and Zhao, 2016).

The basic idea of fuzzy support vector machine can choose different membership degree according to effect degree of different inputting sample on customer relationship management, a group of samples is given, which is defined by IJISSCM.2019040105.m01, where, IJISSCM.2019040105.m02 denotes inputting vector of fuzzy support machine; IJISSCM.2019040105.m03 denotes that IJISSCM.2019040105.m04 belongs to one class of two classes, IJISSCM.2019040105.m05; IJISSCM.2019040105.m06 denotes the membership degree of class concluding the sample, IJISSCM.2019040105.m07 (Zhang et al., 2017). Fuzzy support vector machine has the same object with the support vector machine, the two classes can be divided based on super plane, and make distance between the supper plane and two classes biggest, the mathematical model of fuzzy support vector machine is expressed as follows (Zhang et al., 2015):Objective function: IJISSCM.2019040105.m08IJISSCM.2019040105.m09(1) Boundary condition: IJISSCM.2019040105.m10, IJISSCM.2019040105.m11, IJISSCM.2019040105.m12(2) where, IJISSCM.2019040105.m13 denotes the error measure of support vector machine, IJISSCM.2019040105.m14 denotes the error measure of different weight.

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