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.

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

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 hybrid movie recommendation system developed with data from The Movie Database (TMDb)

🎬 AI-powered full-stack movie recommendation system using TF-IDF + Cosine Similarity | FastAPI + Streamlit | Deployed on Render

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.

This project develops a hotel recommendation system using content-based filtering. By analyzing hotel features such as room types, amenities, and pricing, it provides personalized suggestions for users. The model uses techniques like TF-IDF and evaluates its performance based on Precision@5, achieving high accuracy in recommendations.

This repository will give you insights of how to do traditional algos using scikit learn library in Python from scratch.

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

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

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.

ML | recommendation system | low carbon products

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

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.

Content-based music recommender scoring 18 songs across genre, mood, energy, and acousticness. Includes adversarial profile testing, weight sensitivity experiments, and a model card documenting filter bubble bias. Foundation for VibeFinder 2.0 (RAG)

Content-based Tech Stack Recommendation Engine using TF-IDF and Cosine Similarity to rank personalized technology stacks from user preferences, with Top-N filtering and cold-start handling.

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

Recommender systems

Movie Recommendation System Using Two approaches Collaborative Filtering ( CF ) & Content-Based Filtering ( CBF )