Product Recommendations through Neo4j by Analyzing Patterns in Customer Purchases

Recommendation systems are becoming more important each day as user interaction on the Internet grows in size and complexity. To achieve better user experience and personalized choice of products for each user, it is important to create a recommendation system that takes all the interaction of a user on the Internet and analyzes it thoroughly to get a better understanding of the user. Understanding the user will benefit the business more, as each user will obtain a personalized experience based on how they act. This study focuses on utilizing a graph database to gain insight into user behavior and to develop a recommendation system based on how users act on the Internet. The recommender system will use the Neo4j database as it provides much functionality to work with, such as the Graph Data Science library and the Jaccard Similarity method. Using all the graph technologies that exist today, this study will enable businesses to provide a personalized experience to users by providing detailed, accurate, effective, and efficient recommendations.