Abstract
Given the rapid development of e-commerce, recommendation algorithms play a crucial role in enhancing user experience and promoting platform growth. This paper delves into the application of recommendation algorithms within e-commerce platforms, scrutinizes the advantages and disadvantages of collaborative filtering, content-based recommendation, and hybrid recommendation models, and elucidates how these algorithms can enhance user stickiness, improve conversion rates, and promote the overall growth of platforms through personalized recommendations. Through empirical case studies, the author demonstrates how recommendation algorithms can optimize the user shopping experience and enable e-commerce platforms to differentiate themselves in a fiercely competitive market. This research not only aids e-commerce platforms in improving operational efficiency and market competitiveness but also provides valuable practical insights and theoretical support for the fields of data science and artificial intelligence.
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