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.
Published Version
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