While recommendation systems have gained the popularity in the advanced of internet usage, there has been need for developing systems that will however meet user preferences On the other hand, we specified the choice of Content Based System rather than Collaborative Filtering.

A content-based recommendation system suggests items based on the attributes or features of the item itself and a user's past preferences. This method creates a user profile, which is matched against item features (for instance, genres in movies, or ingredients in recipes) to recommend the most relevant items. Unlike collaborative filtering, which relies on user interactions and similarities among users, content-based recommendation only looks at the direct relationship between user preferences and item characteristics.

Project Overview

This type of recommendation has widespread applications, ranging across industries like: E-commerce: Recommending products similar to those a user has purchased or viewed. Media: Suggesting movies, books, or music based on previously consumed content. Healthcare: Personalizing treatment plans or medication recommendations based on a patient’s medical history. Applications in Business: Driving Sales Forecast and Reducing Customer Churn The Movie Recommendation System can play a crucial role in business by predicting customer preferences and behavior. For instance, a company can utilize such systems to anticipate what types of products or services customers are likely to purchase in the future. By understanding the preferences of users based on their movie-watching habits, businesses can offer personalized deals, forecast demand for specific genres, and align their marketing strategies to boost sales. Moreover, recommendation systems help reduce customer churn, as they keep use

Technology is best when it brings people together.

Matt Mullenweg

Github: https://github.com/antonie-riziki/Hybrid-Movie-Recommendation

Demo: https://hybrid-movie-content-recommendation.streamlit.app/