This movie recommender project is designed to help users find new movies to watch based on their previous viewing preferences. The project uses a bag-of-words approach to analyze the textual content of movie descriptions and synopses to identify similarities and recommend similar movies to users.
The bag-of-words approach used in this project involves breaking down the text content of each movie into individual words and creating a matrix that counts the frequency of each word in all movie descriptions. This matrix is then used to find similarities between movies based on the number of overlapping words. The project also uses a cosine similarity metric to measure the distance between movies in the matrix and recommend movies that are closest in terms of content. The system can recommend a list of top 10 movies based on user input.
Demo:
Overall, this movie recommender project provides a powerful tool for anyone looking to discover new movies based on their interests and viewing history. The bag-of-words approach used in this project is simple yet effective, making it a great starting point for anyone interested in exploring the world of recommendation systems.