用实时爬取数据+混合推荐，让电影推荐更贴合用户口味和流行趋势。
Movie Recommendation using Web Crawling
- 结合网页爬虫与API获取实时电影数据
- 融合内容与协同过滤的混合模型提升推荐准确率
- 适合关注动态内容推荐的研究者与开发者
在数字化时代，流媒体平台提供海量电影，用户难以找到符合个人偏好的内容。本文探索利用先进的HTML抓取技术和API，从热门电影网站实时获取数据，并结合静态的Kaggle数据集训练推荐系统，提升建议的相关性与时效性。通过融合基于内容的过滤、协同过滤及混合模型，系统同时利用历史数据与实时信息，实现更个性化的推荐。实验表明，引入动态数据不仅能提高用户满意度，还能使推荐结果与当前观看趋势保持一致。
原文摘要 · Abstract (English)
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.
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