Personalized Flight Auxiliary Service Package Recommendation

In the context of the Internet and big data,the recommendation system has been widely used in various fields and has become an important part of Internet services.According to the actual demand,the recommended system from the original recommendation of a single product development to simultaneously recommend multiple product package.Booking flights through online travel companies(OTCs)is becoming increasingly popular.In order to improve profits,OTCs often suggest additional optional auxiliary services,such as security insurance,a VIP lounge or a pick-up service,to passengers.In order to promote the sale of auxiliary services,these can be selected as a default when passengers purchase a flight.However,if a passenger does not want to buy these services,he will have to cancel them himself,which can result in a negative user experience.The paper mainly studies personalized flight auxiliary service package recommendation.Auxiliary services relative to the general product has some special features,such as service less features,services can not be compared directly between.In the paper,by analyzing the characteristics of users and their historical data,a personalized service recommendation based on gradient boosting decision tree(GBDT)model is proposed.GBDT is also applied to mine the interrelationships between services so that a service package is finally recommended.In addition,based on the user satisfaction and loss,the paper proposes a Markov decision model for long-term business returns.And use of reinforcement learning,get the maximization of long-term corporate earnings recommendation strategy.Finally,the paper combines the actual needs of tourist service providers,puts forward the framework of personalized flight auxiliary service package recommendation system.The framework includes data storage module,recommended module and background monitoring module.The paper also makes use of the recommended algorithm proposed in the paper to implement the recommended module and tests the performance of the framework.