yash1314 / movie_recomender_system

This repo contains the movie recommender system which uses vectorization, cosine similarity distance methods to calculate the most similar content based on movie tags/info.

Movie_Recommender_System

This is a Movie Recommender system which can recommend movies based on user input.

Technology used:

• python, pandas, numpy
• sklearn, vectorizer
• cosine distance similarity
• streamlit
• tmdb API

Movie Recommender method:

• Vectorization is a process of converting text data into numerical vectors, such as TF-IDF or word embeddings, allowing mathematical operations on text. Cosine similarity is a distance metric that measures the similarity between two text vectors by calculating the cosine of the angle between them, with a higher value indicating greater similarity, making it useful for text matching, recommendation systems, and clustering.

Testing:

• To access the webapp follow the link - https://movierecomendersystem-kn7wte7gavzfwgvce43duk.streamlit.app/