Building a Movie Recommendation System Using Neo4j Graph Database: A Case Study of Netflix Movie Dataset

Recommendations for movies can help discover new and enjoyable movies. This study uses the Neo4j Graph Database to create a recommendation system using the Netflix Movie Dataset. The objective of this research is the development of a movie recommendation algorithm using the k-NN similarity algorithm and FastRP node embedding machine learning. The results have provided recommendations based on similar attributes, such as actors, directors, country, type, and rating. In this research, we used a similarity scale of 0–1 so that we found 200 pairs of movies that were similar between each other and there were 62070 movies that were less similar between each other.