Content Based Recommendation system uses attributes of the content to recommend similar content. It doesn't have a cold-start problem because it works through attributes or tags of the content, such as actors, genres or directors, so that new movies can be recommended right away.

This project is a proof-of-concept news recommender system. It utilizes recommender models to deliver personalized news article recommendations based on user preferences and article characteristics. The project explores data analysis, model development, evaluation, and business application potential, demonstrating the value of tailored suggestions.

A machine learning-based recommendation system designed for an e-commerce platform.

A Movie Recommender System is an application program build using python programming that can recommend you the similar movies according to your search. Streamlit library is used for front-end development

A recommendation system for playstation and x box games .

Buiolding up a recommendation engine for movie with Content based model and collaborative filtering methods.

Welcome to our movie recommendation system project repository! This repository hosts the codebase for our machine learning project focused on developing a movie recommendation system. Our aim is to provide users with personalized movie recommendations.

This project involves developing a content-based recommendation system that utilizes advanced machine learning techniques to suggest movies similar to the user's preferences and watching history.

System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory.

A Content-based movie recommendation system that recommends movies to a user by using the similarity of movies. This recommender system recommends movies based on their description or features. A useful application of machine learning in the Media/communication Industry

Tecknosoft's Intern TASK : Movies Recommender System and deploying it in huggingface .

Hybrid movie recommender using Twitter handle and content-based filtering.

Proposing a novel approach to music recommendation that takes the audio of the user as input, converts it to text and performs sentiment analysis, tf-idf, and normalization. We then use content-based and collaborative filtering techniques to recommend songs

This project is a corporate partnership with the online bookstore platform 'YES24', where we collect data from various platforms such as YouTube to analyze the latest trends and develop a service that recommends books matching these trends.

I developed a simple content-based recommendation system that suggests movies to users based on their preferences. Users can enter a movie they like, and the system recommends other movies with similar genres. This project helped me understand the basics of recommendation systems and content-based filtering techniques.

Recommender systems

The application uses content based filtering to make recommendations. For every movie selected, 12 recommendations are made based on their cosine similarity with the selected movie. An API feteches the poster image of the movie and displays them in an image grid to the user The database offers nearly 5000 movies to select from

The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.

I created movie recommender system using content based filtering.