Research on Intelligent Recommendation Method of e-commerce Hot Information Based on User Interest Recommendation

In order to improve the effect of information intelligent recommendation, this paper puts forward the optimization research of hot information intelligent recommendation method based on user interest recommendation. The quality of user collecting modeling directly determines the quality of personalized recommendation by collecting user interest and making intelligent recommendation based on information classification model. The basic process, principle and algorithm of personalized recommendation system are studied. Based on the algorithm principle of user data collection, user model representation, user model learning and user model updating, this paper optimizes the recommendation methods, completes the design and implementation of the intelligent recommendation engine in the special system. The recommendation system simulates the store salesperson to provide product to customers and help users find the required information. Finally, through the experiment, it is proved that the intelligent recommendation method of e-commerce hot spot information based on user interest recommendation has improved the customer satisfaction and the effectiveness of recommendation information in the practical application process, which fully meets the research requirements.