Personalized Algorithmic Recommendation: An Analysis of the Problems and Solution Paths in the Context of New Media

大数据时代，数据挖掘技术催生了个性化推送。新媒体平台上，算法推荐根据不同的受众群体的个性化需求精准推送符合他们喜好的内容。一方面，它大大提高了信息传播的有效性、及时性、准确性；但另一方面，由于大数据会记录用户的个人偏好，会将相似内容局限于用户感兴趣的“圈”中，同样会产生诸多负面影响及面临伦理层面的质疑。本文旨在分析算法推荐技术带来的负面效应并探索其解决方案。In the era of big data, data mining has given rise to personalized content delivery. On new media platforms, algorithmic recommendation caters to diverse user preferences by precisely distributing content that aligns with their tastes. This approach enhances the efficiency, timeliness, and accuracy of information dissemination. However, as big data tracks user preferences, it may confine users to a “cocoon” of similar content, sparking various negative influences and ethical concerns. This paper delves into the adverse impacts of algorithmic recommendation and proposes solutions to address these challenges.