Research on personalized recommendation algorithm based on user preference in mobile e-commerce

With the development of Internet, the problem of information overload becomes more and more serious. The personalized recommendation technology can establish user files through the user's behavior and other information, and automatically recommend the items that best match the user's preferences, thus effectively reducing the information overload problem. Based on this, this paper studies the personalized recommendation algorithm based on user preferences in mobile e-commerce. In this paper, user preference model under UTA algorithm is constructed on the basis of user rating on multiple criteria of the project, and user preference clustering is used to improve the scalability problem of personalized recommendation. Finally, the simulation is conducted according to the proposed personalized recommendation algorithm based on user preference. The simulation data use the multi-criteria rating data from 6078 users of Yahoo! Movies website for 976 movies (including 62,156 rows of data). The experimental results show that the multi-criteria recommendation algorithm (MC-CF-dis), which uses user distance similarity, has the best effect, and the MAE and RMSE value of this algorithm is about 1.2 lower than that of the other three algorithms. Accuracy is 6-10% higher than other algorithms. Thus, using this personalized recommendation algorithm based on user preference can effectively improve the quality of recommendation.