| Consumer network information behavior analysis is an essential part of online tourism product design and supply.It also plays a big role in research of consumer behavior theory.Since the end of the last century,relying on Behavioral Economics,Psychology,Informatics and other multi-disciplinary scientific knowledge accumulation,domestic and foreigner scholars have researched tourist network information behavior from different aspects,including but not limited to behavior intention,behavior process,mechanism and influencing factors of behaviors.However,in the specific times and technical background,the practical researches about the consumer behavior in a certain business scenario are less estimated.In most cases,these researches are from single research perspective,either serving consumer demand or serving enterprise product supply.Therefore,this study puts' forward a practical research on consumer behaviors in the context of actual online tourism business scenario.It is focusing on connotation and goal of personalized recommendation in this context and technically supported by big data mining and its application.The development of specific logical ideas includes:First perform definition and classification according to every behavioral event of online tourism users(including potential and actual consumers).Further,aggregate consumer needs and preferences based on definition and classification,and customize online tourism products as personalized recommendation.It explores the mechanism converting online tourism to offline deal guided by the online personalized recommendation,and the logical rule and interacting space between online tourism user to enterprise or vice versa.Last but not less,it gives the strategic research direction of online tourism personalized recommendation.Based on aforementioned logic,the main contents of this paper and the main problems to be solved include three aspects:First,This study is an interdisciplinary research about "Internet + tourism";second,This study is an exploratory study focusing on tourism user behavior classification,utilizing personal recommendation as implementation,and exploring interactive space between users and products and its logical rules;third,through the implementation of online tourism personalized recommendation,it is a directive research inspiring enterprise management strategies.This study,based on its theory and practice,ultimately identifies the interdisciplinary and practical framework and methods-online tourism personalized recommendation design.It is technically based on data mining and application,in context of online tourism business scenario.Its goal is to provide ultimate experience for users,and precise marketing for enterprises.Its personalized recommendation implementation can dramatically improve interaction between users and products,then also the conversion rate from online to offline.Hence,it provides enormous business value.Based on the above research logic,content and process,the main conclusions of this study include the following four aspects:(1)The complexity of the online business environment and the particularity of the tourism activities determine the interaction between the online tourism user and the enterprise.There are complex cognitive processes and behavioral processes in this interaction.Through the personalized approach and method,this study establishes the online design and supply of tourism products exist the logical space of "gathering user needs and reverse customization",and there is the possibility of interactive design between online tourism users and products,and can be implemented by means of information technology.(2)Through the comparison of current mainstream personalized recommendation methods,this study put forward the current method needs to be combined and improved for actual business scenario of online tourism.The basis of the combination is utilizing the advantages of each recommended method are complementary,and the improvement comes from narrow down the scope of application to actual online tourism business.Finally,the method of adding the implicit semantic model,the context-aware filtering and other methods and techniques to supplement the existing personalized recommendation method,and carried out a detailed process research.(3)Under the guidance of consumer behavior theory and context theory,this study constructs the model framework of online tourism personalized recommendation program based on user behavior analysis.It clarifies concepts including online tourism user behavior and generated data,user preference theme and space,user model and user's image,user-product two-dimensional matrix,etc.It also clears the logical levels and association among them,verifies the similarity measurement and calculation,and optimizes the implementation of the personalized recommendation program under online tourism business scenarios.(4)According to the framework of the online tourism personalized recommendation in this study,the individual recommendation method is adopted as the main approach to improve the user's viscosity and enhance the enterprise knowledge innovation ability,and put forward the directional strategy recommendations to the future operation and management of the online tourism enterprise:Emphasis on user value assessment,Recreate the value of business,and Strengthen the personalized precision marketing.This study attempts to be innovative in the following areas:(1)In the perspective of study,based on the tourist network information behavior theory,online reputation theory,online commentary theory and other multi-angle perspective,this study expands the existing single research perspective of consumer behavior,and focus on the interactive space of consumer and business.It constructs the overall model framework of the online tourism personalized recommendation scheme,and clarifies the logic between the data layer,the trigger layer,the model layer and the business layer,with level and relevance among them.The view of this study is more open,the content is more abundant.(2)In the method of study,it carries out data collection and processing of the massive online user behavior information by using experimental observation(screen tracking),depth interview,network data mining and measurement statistics.Further,it defines and classifies the user's interest and preference,calculates and measures the similarity between the user and the product.The technics this study introduced,especially the method of network data mining,compensates the lack of data by traditional "after the tour" questionnaire survey,and lack efficiency by general measurement statistics on massive data.It provides a more intuitive and solid base of data and basis for the online tourism personalized recommendation model framework.(3)In the process of study,it constructs the online tourism user model based on user behavior analysis,and obtains the complete tag system of online tourism user image.Under the support of data in the experiment,it is found that the personalized recommendation method has limitation on the actual business scenario of online tourism.Finally,it proposes to add the context-aware filtering technology to perfect and supplement the individual recommendation scheme.(4)In the result of study,it evaluates the proposed online tourism personalized recommendation scheme.The feasibility and optimization ability of the scheme were considered.It was verified that the situational awareness factors are the behavioral variables which must not be ignored in the process of online tourism personalized recommendation.The conditional screening and filtering on behavioral variables can significantly improve the efficiency of the individualized recommendation scheme. |
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