Research on Personalized Recommendation Algorithm Based on Cluster Analysis and Artificial Intelligence

The demand for personalized recommendation systems is gradually increasing under the backdrop of big data. This paper aims to enhance the effectiveness of personalized recommendation algorithms, better cater to users' individual needs, and provide support for constructing an improved content recommendation system. It proposes that a personalized algorithm based on cluster analysis and artificial intelligence can address the cold start problem faced by traditional recommendation algorithms, thereby further enhancing the efficacy of personalized content recommendations and user satisfaction. Additionally, it introduces the fundamental concept and implementation process of the algorithm while evaluating and analyzing its performance. Experimental results demonstrate that the proposed algorithm outperforms traditional approaches in terms of accuracy, stability, and efficiency. The research findings not only enrich existing theoretical advancements in personalized recommendation algorithms but also offer decision-making guidance for enhancing recommendation system accuracy.