Mobile Commerce Service Recommendation Based On Position And Preference Model

With the popularity of mobile Internet, people's living becomes further "Internetize". In order to meet the needs of mobile users anytime, mobile applications need to provide services to users by multiple dimension at the same time, especially in the emerging field of mobile health, mobile finance. Overall, the mobile Internet business can be divided into: mobile online payment, online shopping, online banking and travel booking and other mobile applications, they all grow at an annual rate of over 40%. Mobile Internet users are the main driving force, which provides a good foundation for the further development of mobile commerce. The service object of the mobile commerce has a high degree of service and mobility compared with the traditional electronic commerce. Therefore, in order to adapt to the new characteristics of the mobile commerce, it is necessary to meet the needs of users to purchase the basis of the user to customize service. In view of this situation, this paper has carried on the thorough research to the service recommendation system in the mobile commerce.The main work of this paper is as follows:First, this paper expounds the significance of developing service recommendation in the mobile commerce environment, and summarizes the research status at home and abroad from the point of view of the dynamic adaptation of the recommendation algorithm to the change of user interest. At the same time, this paper analyses the service recommendation algorithm from the two aspects of position and browse path deeply according to the characteristics of mobile commerce users.Second, using the advantage that the system can obtain user's geographical position in real time the mobile commerce environment, introducing distance variable into the traditional recommendation algorithm. According to the different users' browsing record, adjust distance variables in a real-time, then recommend the most suitable product to the user. This method solves the problem of short-term interest change caused by the change of the external information.Third, based on the algorithm which introducing position to solve short-term interest, taking into account the sparsity and cold start problem of user data sets in the mobile commerce environment. This paper uses ant colony algorithm to pre populate the data set, and improves the recommendation accuracy.Finally, verifying the feasibility of introducing distance variable and ant colony algorithm in mobile commerce by simulation experiment. Experiments show that after introducing the distance variable recommendation system can provide users with more sensitivity to suit the current service environment, using browsing path algorithm based on ant colony algorithm can effectively improve the accuracy of recommendation.