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.

**Movie Recommender System 🎬📽** | A machine learning-based recommendation system that suggests movies based on user preferences. Built using **Python, Pandas, Scikit-Learn, and NLP**, it utilizes content-based filtering to provide personalized movie recommendations. 🚀

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

ML | recommendation system | low carbon products

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.

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.

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

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 .

A recommendation system for playstation and x box games .

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

Demonstration on different types of recommender systems

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

A recommendation system leveraging similarity measures to generate top-N item recommendations for various applications..

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.