The Design And Implementatio Of News Recommendation System Based On User Access Sequence

In recent years,with the rapid development of the Internet,the network information is also growing exponentially.Due to people’s limited time and energy,people can not quickly read the news content they are interested in.In order to give users a better reading experience,it is more important and significant to model the user’s reading interest preference.Research shows that human behavior has a sequence pattern and the user’s historical access records can always reflect the user’s interest preference.Therefore,making full use of the user’s access sequence helps us to better establish the interest preference model for it.The main contents of this paper are as follows:(1)In this paper,a deep reinforcement learning model of news recommendation based on attention perception is proposed.The model integrates user access sequence into deep reinforcement learning model,combines neural attention network mechanism and public preference,and fully excavates user’s interest preference.Experiments on two real datasets show that the model can effectively improve the performance of recommendation.(2)In this paper,an Multivariate hidden vector based on attention optimization model for click-through rate prediction is proposed.The Model uses attention perception to construct multiple hidden vectors;Then,a neural attention network model is constructed by integrating low dimensional features and high dimensional features.Experiments on two real datasets show that the model can effectively improve the performance of recommendation.(3)This paper proposes a news recommendation model based on the attention of similar news sequences.Firstly,this model proposes a novel method to extract user information and build groups for users;Secondly,we construct the attention factor of similar news sequences,and integrate the model with the news popularity factor.Experiments on two real datasets show that the model can effectively improve the performance of recommendation.(4)This paper proposes a deep learning model of news recommendation based on access sequence.When generating recommendation list,the model will not only consider deep learning,but also consider the user’s access sequence.Experiments on two real datasets show that the model can effectively improve the performance of recommendation.(5)Based on the four innovative models,this paper designs and implements a news recommendation system based on user access sequence.The system can recommend for different user categories,and effectively model the user’s personalized interest.