Research On Personalized Recommendation Mechanism Based On Online Lifestyle And Integrated Value

Because of the rapid development of Internet, the Internet information service has changed from the traditional index mode to the search mode, and then to the social networking mode. The fourth reform has already started, which is the transition from the search mode to the recommendation mode. On one hand, how consumers can an appropriate or favorite product among tons of choices has been one reason of inhibiting consumption. On the other hand, for the e-commerce companies, the problem is how to target customers to improve potential customer value. The heterogeneity of consumers and information overload has led the enterprise to do personalized recommendation. It has generally become the most challenging trend for traditional e-commerce since the information era began. However, the establishment of relationships between personal products and potential customers at an appropriate time and channel is still a major issue.The traditional recommendation method is primarily dependent on the consumers’ external behaviors, which is low in accuracy and efficiency. Some intrinsic factors within consumers are more reliable, which can be used for personalized recommendation. Currently, a good recommendation method based on consumers’ intrinsic factors is not available. The individual behavior of the consumer is independent and scattered. It is necessary to find a novel recommendation method. As a reflection of real lifestyle, online lifestyle shows customers’ behaviors, interests and thinking, and offers an alternative idea for personalized recommendations with high efficiency. In mobile commerce, there is a special character named scenario effect. It is necessary to construct a scenario model on the basis of scenario valuation. Therefore, this work proposes a personalized recommendation depending on online lifestyle and comprehensive value. It includes:(1) Devising a scale to measure online lifestyle of customers in China. Consumers in Beijing are selected as the research sample, and then the research designs an online lifestyle scale of Chinese consumers. Seven factors are finally classified and concluded as following:moderation, entertainment, luxury, tradition & conservation, rationalism & modesty, fashion sense, and social activity. A structural equation model is subsequently configured to investigate the influences of online lifestyle on the purchasing behavior of consumers.(2) Establishing a client model from the perspective of consumers’ interest and value, and another product model based on products’ value. The personalized recommendation in e-commerce relies on the consumers’ online lifestyle, The behavior taggings are adopted to identify consumers’ online lifestyles, which bridge products and potential customers.(3) Analyzing the differences between mobile commerce and e-commerce. The consumption in mobile commerce shows four features:micro-consumption, long tail effect, external effect and scenario effect. According to different scenarios in mobile commerce, we use an evolution game to construct a scenario model on the basis of scenario valuation. Finally, a comprehensive recommendation mechanism is formed, which considers customer value, the product value and scenario value. The theory and methodology of a personalized recommendation for integration value in mobile commerce is also established.(4) Using the personalized recommendation based on online lifestyle in an enterprise. It provides a higher efficiency compared with recommendation methods based on behaviors, with feedback rate as the evaluation standard.The innovations of this work are as follows:(1) A novel recommendation idea. The personalized recommendation is proposed based on the online lifestyle. The traditional personalized recommendation relies primarily on the behaviors of people. It is extensive and of low efficiency. This paper designs an online lifestyle scale of Chinese customers, which is an original creation. Behavior tagging is suggested. The tagging includes information importance and timing to identify the intrinsic online lifestyle of consumers. This is an extension and innovation of the idea about personalized recommendation.(2) A novel recommendation theory. The customer value, product value and scenario value are considered in the user, product and scenario models. The traditional personalized recommendation is limited, only focusing on the customer interest and demand, the product attributes and the application scenario. This work combines customer value, product value and scenario value together. It is an academic innovation in the theory of personalized recommendation.(3) A novel recommendation method. The personalized recommendation mechanism is proposed by integrating a series of comprehensive values. The recommendation method is definitely different from traditional ones since customer value, product value and scenario value are all considered together. Besides the match of customers’ demand and product attributes, the improvement of the integration value is also indispensable. Therefore, the recommendation not only aims at high accuracy, but also maximizes the integration value, which is a comprehensive evaluation. It is an innovation of the personalized recommendation method to traditional mechanisms.This work investigates the personalized recommendation mechanism based on online lifestyle. It contributes to the application of online lifestyle in the personalized recommendation. It is also a fundamental support for the development of personalized recommendation in mobile commerce. Finally, It offers a new method for high-efficiency personalized recommendation in the enterprise practice.