IJIRCSTJournal International Journal of Innovative Research in Computer Science and Technology I S Volume 11 Issue 3 Computer Science and Engineering English May - June 2023 N N Y 2023 06 15 Computer Sciences Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations English Y 67 71 Shweta Sinha English Y Treya Sharma English N https://doi.org/10.55524/ijircst.2023.11.3.12 With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection. English Content-based recommendation system, Movie recommendation, Text vectorization, Cosine similarity, Personalized recommendations, Bag of words, Semantic relationships https://ijircst.org/abstract.php?article_id=1141