The Research And Application Of Personalized Recommendation Algorithm For O2O User Behavior Analysis

In recent years,with the development of Internet e-commerce,users in the electricity business platform and the use of the data soared,so tha t users face serious information overload.Personalized recommendation system is one of the effective ways to solve information overload.This paper uses the collaborative filtering recommendation algorithm and the content-based recommendation algorithm to build the personalized recommendation system,and mix the two recommendations to produce the final recommendation.In the design of collaborative filtering recommendation algorithm based on users,different similarity measure methods are compared,and finally used the extended cosine similarity to calculate the similarity between users.In the content-based recommendation algorithm,the cosine similarity theorem is used to calculate the similarity between the feature vector and the project eigenvector.The recommendation algorithm based on content and collaborative filtering is taken into account in terms of user personal behavior and neighboring user group factors,which improved the recommended effectiveness.Besides,The similarity between projects is obtained by calculating the similarity between the structured data of the project.Based on the analysis of user behavior oriented to O2 O,this paper presents a hybrid recommendation algorithm for the behavior analysis of O2 O users.On the basis of content-based and collaborative filtering recommendation algorithm,a hybrid recommendation module architecture is designed for O2 O user behavior analysis.The effectiveness of the hybrid recommendation algorithm in the life information service system based on O2 O is verified by the combination of theory and Practice.Through the combination of investigation and analysis of user behavior,summarizes the res earch status in the analysis of user behavior based on the life information service system in O2 O,and looks forward to the development trend and technology growth point in this field.