This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific techniques of collaborative filtering. After introducing the recommendation system and collaborative filtering algorithm, this paper elaborates on the theoretical basis and technical principles of the recommendation algorithm based on cognitive ability and difficulty and provides an in-depth analysis of the design and implementation of the recommendation algorithm by combining cognitive diagnosis theory, readability formula, and English knowledge map, which provides a comprehensive and solid theoretical guidance and support for the application development of the online English learning platform. The system is tested by building a Spring Cloud platform, importing actual business data, focusing on the validation of the recommendation model, and connecting the recommendation system to the formal production system to analyse the recommendation effect. Compared with the original recommendation method, the online English learning platform designed and implemented in this paper based on the cognitive ability and difficulty collaborative filtering recommendation algorithm has a better recommendation effect. The system is proved to be well designed and has certain reference and guiding value for the whole web-based online learning platform and has a broader application prospect nowadays and in the future.

中文翻译：

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基于协同过滤算法的英语学习平台优化

本文详细介绍了通过协同过滤算法优化英语学习平台的推荐系统和协同过滤算法，并分析了协同过滤的算法原理和具体技术。在介绍了推荐系统和协同过滤算法之后，本文基于认知能力和难易程度阐述了推荐算法的理论基础和技术原理，并结合认知诊断对推荐算法的设计和实现进行了深入的分析。理论，可读性公式和英语知识图谱，为在线英语学习平台的应用开发提供了全面而扎实的理论指导和支持。通过构建Spring Cloud平台，导入实际业务数据，关注推荐模型的验证以及将推荐系统连接到正式生产系统以分析推荐效果，对系统进行了测试。与原始推荐方法相比，本文基于认知能力和难度协同过滤推荐算法设计并实现的在线英语学习平台具有更好的推荐效果。该系统设计合理，对整个基于网络的在线学习平台具有一定的参考和指导价值，在当今和将来都有广阔的应用前景。并将推荐系统连接到正式生产系统以分析推荐效果。与原始推荐方法相比，本文基于认知能力和难度协同过滤推荐算法设计并实现的在线英语学习平台具有更好的推荐效果。该系统设计合理，对整个基于网络的在线学习平台具有一定的参考和指导价值，在当今和将来都有广阔的应用前景。并将推荐系统连接到正式生产系统以分析推荐效果。与原始推荐方法相比，本文基于认知能力和难度协同过滤推荐算法设计并实现的在线英语学习平台具有更好的推荐效果。该系统设计合理，对整个基于网络的在线学习平台具有一定的参考和指导价值，在当今和将来都有广阔的应用前景。本文基于认知能力和难度协同过滤推荐算法设计并实现的在线英语学习平台具有较好的推荐效果。该系统设计合理，对整个基于网络的在线学习平台具有一定的参考和指导价值，在当今和将来都有广阔的应用前景。本文基于认知能力和难度协同过滤推荐算法设计并实现的在线英语学习平台具有较好的推荐效果。该系统设计合理，对整个基于网络的在线学习平台具有一定的参考和指导价值，在当今和将来都有广阔的应用前景。