/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.

Primary LanguagePython

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: