Cloud Platform Business Scenario Modeling Based on Multi-Model Fusion Recommendation Algorithm

In the era of cloud computing, businesses are increasingly relying on cloud platforms to streamline their operations and deliver services efficiently. Recommendation systems play a pivotal role in suggesting suitable services and resources to enhance user experience and optimize resource allocation. This paper presents a novel approach, the Multi-Model Fusion Recommendation Algorithm (MMFRA), which integrates multiple recommendation models using advanced fusion techniques to enhance the accuracy of recommendations in cloud platform business scenarios. The implementation process of MMFRA involves combining diverse recommendation models, such as collaborative filtering, content-based filtering, and matrix factorization, into a unified framework that leverages their strengths while mitigating individual limitations. This fusion process is designed to achieve higher precision in service recommendations by considering various aspects of user behavior and preferences. Through a comprehensive evaluation in a simulated cloud platform environment, MMFRA demonstrates superior performance in terms of recommendation accuracy and user satisfaction. The proposed algorithm offers significant potential for enhancing the effectiveness of cloud platform services, ultimately benefiting both service providers and users.