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@article{174725,
        author = {Dr.R.S.Karthik and Mr S.Ponvishnu and Mr B.Nithish Kumar},
        title = {MOVIE RECOMMENDATION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1309-1311},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174725},
        abstract = {In today’s world of vast entertainment content, finding the right movie to watch can be overwhelming. Recommender systems have become an essential tool to solve this problem, guiding users by suggesting movies they are likely to enjoy based on their previous interests or content similarity. This project leverages content-based filtering and cosine similarity to analyze movie features and suggest the most relevant recommendations.

This movie recommendation system allows users to find the movies in the website called CinePulse, it takes them to the movie page and the users can find the details of that movie such as rating, cast, release date, genre etc., whatever the data available from IMDB website holds about the movie. The main part of this project, it recommends similar movies based on content-based filtering and cosine similarity. 

Cosine similarity measures how similar two vectors are by calculating the cosine of the angle between them. Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback. These are used to identify the value of similarity between the movies which are then used to recommend the users below.},
        keywords = {Movie Recommendation System, Content-Based Filtering, Cosine Similarity, TMDB API, Machine Learning, Flask},
        month = {April},
        }