Research On Collaborative Filtering Recommendation Algorithm Based On News Timeliness Model

With the rapid development of network society and the increasing scale of network news data,it is difficult to mine knowledge in the process of news production;It is difficult for news consumers to quickly find the news in line with their own interests in the massive network news.Therefore,the research on the processing strategy of network news data has important application value.Accurate network news topic clustering algorithm brings efficient news mining,and provides convenience for the research of personalized recommendation algorithm.This paper focuses on the topic clustering in the process of online news production and personalized recommendation in the process of news consumption:(1)For the common problems in topic clustering of network news documents,such as unclear semantics and clustering algorithm can not be updated incrementally,a single pass clustering algorithm based on LDA topic model is proposed.LDA topic model is combined with single pass to update incremental data.When selecting clustering centroid and initial clustering distribution,the selection of clustering centroid is adjusted by news heat,and then the stable pre clustering results provided by LDA topic clustering algorithm are used to improve the effect of news topic clustering algorithm.Simulation results show that the algorithm can improve the accuracy of clustering algorithm,and is suitable for incremental update.(2)Aiming at the problems of poor news timeliness and outdated news redundancy in the input set of recommendation algorithm in the network news recommendation system,a network news information aging model is proposed based on the literature aging model.In the third chapter,we use the single pass clustering algorithm based on LDA topic model to get the clustering set.First,we calculate the average propagation time in a news topic clustering set.Combined with the negative index model,we calculate the aging rate after news release.As a timeliness parameter,we improve the user based collaborative filtering algorithm,so as to improve the recommendation effect.The simulation results show that the algorithm can improve the effect of collaborative filtering recommendation algorithm.(3)A personalized news recommendation system is implemented by integrating news document topic clustering and collaborative filtering recommendation algorithm.The function and performance of the system are tested.After testing,the function of the system is perfect,which can quickly collect news data for processing,and personalized news recommendation for different users.