In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends.

翻译：在当今数字世界中，流媒体平台提供了海量电影资源，使得用户难以找到符合个人偏好的内容。本文探讨了如何利用先进的HTML爬取技术与API，整合来自热门电影网站的实时数据。同时，系统结合了基于静态Kaggle数据集训练而成的推荐模块，以提升建议内容的相关性与时效性。通过融合基于内容的过滤、协同过滤以及混合模型，我们构建了一个能够同时利用历史数据与实时数据来生成更个性化推荐的系统。我们的方法表明，引入动态数据不仅能提升用户满意度，还能使推荐结果与当前观影趋势保持同步。