Research on Context-Awareness Mobile Tourism E-Commerce Personalized Recommendation Model

E-commerce personalized recommendation problem in social network based on context is of great realistic significance to users and merchants. Aiming at data sparsity and low precision of personalized recommendation in tourism E-commerce personalized recommendation model integrating multivariate social information, this paper integrates social information such as trust relationship between users, time and geographic position of commodity purchasing into traditional collaborative filtering recommendation mode based on users and proposed context-awareness mobile tourism E-commerce personalized recommendation model-MTERec, which digs interest and preference of users under different contexts, calculates weight of user interest from perspective of mobile environment context where the user is located and finally refers to idea recommended by collaborative filtering recommendation to realize rating prediction of users to commodities and recommend according to interest and preference of the users. Experimental results indicate that compared with existing similar algorithms, context- awareness mobile tourism E-commerce personalized recommendation model and algorithm proposed in this paper have higher recommendation precision and user’s satisfaction degree.