Mobile Terminal Users Network Behavior Analysis And Application

With the rapid development of the mobile Internet,mobile terminal devices have been fully integrated into our daily life,work,study,entertainment and social activities,and it is difficult for users to obtain effective resources from massive behavioral information.How to mine the network behavior of mobile terminal users and provide accurate personalized recommendation services has become an urgent problem for operators to solve.However,the traditional users behavior analysis starts from a single scenario and does not consider the user's behavioral habits due to time factors or external environment.changes,resulting in personalized recommendations that do not conform to the actual behavior of users.Therefore,this thesis analyzes the network behavior characteristics of mobile terminal users from the aspects of Internet access time and access content,and uses the back propagation neural network to optimize the average score-based prediction scoring formula in the collaborative filtering recommendation algorithm.For operators,the prediction results can be used.Developing a recommendation list of personalized network services for users can help improve users' stickiness and satisfaction with network applications.In order to realize the mining of mobile terminal users' network behavior characteristics,this thesis analyzes the users' online time period and access content.First,the Euclidean distance is used to measure the similarity of users' online time.Through verification,it is found that the users' online time has a similarity law at the maximum online period.Based on this,hierarchical clustering is used to mine four behavioral patterns of users' online time.Second,k-means clustering algorithm mines 7 behavior patterns of users accessing content;finally,correlates the 4 behavior patterns during the users' surfing period with the7 behavior patterns of accessing content,and obtains the surfing behavior characteristics of users in different time periods.In order to improve the Internet experience of mobile terminal users and enable them to achieve more accurate personalized network services.In this thesis,the termfrequency–inversedocumentfrequency algorithm is used to calculate the users' preference for the network service category.In view of the low accuracy of the prediction score of the traditional recommendation algorithm,this thesis combines the back propagation neural network with the collaborative filtering recommendation algorithm to optimize the prediction score,and use it to optimize the prediction score.Compared with improved collaborative filtering recommendation algorithm and the collaborative filtering recommendation algorithm based on sentiment analysis.The research results show that the root mean square error and mean absolute error of the collaborative filtering recommendation algorithm improved by the back propagation neural network are smaller than those of the collaborative filtering recommendation algorithm based on the improved similarity and the collaborative filtering recommendation algorithm based on sentiment analysis.Therefore,the personalized recommendation algorithm used in this thesis is the recommendation algorithm can improve the accuracy of rating prediction more effectively and get better results.Finally,the application value of personalized recommendation algorithm in three aspects: advertising precision placement,network information precision push and e-commerce marketing is proposed.