Research and optimization of personalized news recommendation algorithm in mobile application
This paper focuses on the research and optimization of personalized news recommendation algorithm in mobile application,aiming to improve user experience and the accuracy of news recommendation.The research primarily focuses on content-based recommendation algorithms,with an emphasis on tags.The overall process of the algorithm includes data preparation,feature extraction,similarity calculation,and recommendation result generation.The algorithm designed in this paper is implemented using the Python language with support from libraries such as NumPy and Pandas.Through this research,practical technical methods for personalized news recommendation in mobile applications are provided,thereby enhancing user satisfaction and engagement.
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