Content-based Recommendation Engine for Video Streaming Platform

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.