移动应用中的个性化新闻推荐算法研究与优化
Research and optimization of personalized news recommendation algorithm in mobile application
王晋1
作者信息
- 1. 安徽广播电视台,安徽 合肥 230071
- 折叠
摘要
文章针对移动应用中的个性化新闻推荐算法进行了研究与优化,旨在提高用户体验和新闻推荐的精准度.文章重点研究内容推荐算法,该算法以标签为重点,算法整体过程包括数据准备、特征提取、相似度计算、推荐结果生成.所设计的算法通过Python语言以及NumPy、Pandas等库的支持得以实现.该研究可为移动应用中的个性化新闻推荐提供实用的技术方法,从而提升用户的满意度和参与度.
Abstract
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.
关键词
个性化新闻推荐/移动应用/内容推荐/余弦相似度Key words
personalized news recommendation/mobile applications/content recommendation/cosine similarity引用本文复制引用
出版年
2024
NETL
NSTL
万方数据