Movie Recommendation System

Purpose: The primary objective of this project is to develop a robust movie recommendation system that enhances user experience by accurately suggesting movies based on their preferences. Leveraging techniques in machine learning and data processing, the system aims to deliver personalized recommendations with improved accuracy and speed.

Scope: The movie recommendation system is a crucial component of streaming platforms, serving to personalize the user experience, optimize content discovery, and maximize user engagement. By employing advanced machine learning algorithms, the system tailors movie suggestions based on individual viewing history and preferences, alleviating decision fatigue and fostering a positive viewing experience. This not only enhances user satisfaction but also contributes to platform revenue by retaining subscribers and attracting new ones. Additionally, the system provides valuable data insights for data-driven decision-making, helping platforms stay competitive in a crowded market. Overall, the movie recommendation system is a key tool in simplifying content navigation, increasing user retention, and ensuring a competitive edge in the dynamic landscape of online streaming.

Feature extraction is the process of converting raw data(information about movies) into a format that can be used for machine learning. In the movie recommendation system, features include various attributes of a movie, such as genre,keywords,director,actors and tags. The goal is to represent each movie as a vector in a high-dimensional space, where similar movies are close to each other.

Cosine similarity is a metric used to measure how similar two vectors are. In the recommendation system,cosine similarity to compare the feature vectors of different movies. The cosine similarity ranges from -1 to 1, where 1 indicates identical vectors, 0 indicates orthogonal (completely dissimilar) vectors, and -1 indicates diametrically opposed vectors.