Recommendation engines suggest content, products, or services to the user by using machine learning algorithms. This paper proposes a content-based recommendation engine that provides personalized video suggestions based on users' previous interactions and preferences. The engine uses TF-IDF (Term Frequency-Inverse Document Frequency) text vectorization technique to evaluate the relevance of words in video descriptions, followed by the computation of cosine similarity between content items to determine their degree of similarity. The system's performance is evaluated using precision, recall, and F1-score metrics. Experimental results demonstrate the effectiveness of content-based filtering in delivering relevant and personalized video recommendations to users. This approach can enhance user engagement on video streaming platforms and reduce search time, providing a more intuitive, preference-based viewing experience.

翻译：推荐引擎通过运用机器学习算法向用户推荐内容、产品或服务。本文提出一种基于内容的推荐引擎，该引擎根据用户历史交互行为与偏好提供个性化视频建议。该引擎采用TF-IDF（词频-逆文档频率）文本向量化技术评估视频描述中词汇的相关性，继而通过计算内容项间的余弦相似度以确定其相似程度。系统性能采用精确率、召回率与F1分数进行评估。实验结果表明，基于内容的过滤方法能有效为用户提供相关且个性化的视频推荐。该方法可提升视频流媒体平台的用户参与度，减少搜索时间，从而提供更直观、基于偏好的观看体验。