Recommendation engine suggest content, product or services to the user by using machine learning algorithm. This paper proposed a content-based recommendation engine for providing video suggestion to the user based on their previous interests and choices. We will use TF-IDF text vectorization method to determine the relevance of words in a document. Then we will find out the similarity between each content by calculating cosine similarity between them. Finally, engine will recommend videos to the users based on the obtained similarity score value. In addition, we will measure the engine's performance by computing precision, recall, and F1 core of the proposed system.

翻译：推荐引擎通过机器学习算法向用户推荐内容、产品或服务。本文提出了一种基于内容的推荐引擎，根据用户过往的兴趣与选择提供视频推荐。我们将采用TF-IDF文本向量化方法确定文档中词语的相关性，进而通过计算余弦相似度获取各内容之间的相似度。最终，推荐引擎将依据所得相似度分数向用户推荐视频。此外，我们将通过计算所提系统的精确率、召回率及F1值来评估引擎性能。