Abstract
With the arrival of the big data age, and users can't effectively find information that matches their interest in the vast quantities of data. The common personalized news recommendation system is based on the user - based collaborative filtering algorithm. However, the content coverage recommended by this keyword based recommendation is narrow and cannot meet users' requirements for the diversity of recommendation systems. For textual data like news, it contains semantic attributes. Therefore, this paper proposes a LDA-LR personalized news recommendation method, which uses the LDA theme model to train the theme distribution of each news, then calculates the user similarity based on the topic, and then combines the LR logistic regression model to filter, and obtains the final news recommendation list.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
