NEO4J Recommendation Engine

Project overview Link to heading

Benchmark study of the Neo4j graph database management system, leveraging its powerful Graph Data Science library to develop a recommendation engine. The benchmark was conducted using a real-world dataset of 1.5 million beer reviews from Kaggle, providing a valuable use case for the technology.

Similarity algorithms were written in Cypher, a practical application of the concepts of the Neo4j Certified Professional certification.

The project involved the importation of the dataset, the creation of a database structure with nodes and relationships, and the implementation of the recommendation algorithm. The algorithm was designed to find similar beers for a given beer by utilizing a multi-step process:

1. Select the beers that have been rated by users who have also reviewed the target beer.
2. Filter this list of beers, keeping only those that have received a rating of at least 4/5.
3. Sort the filtered list according to the number of times each beer appears in the list, providing the top recommended beers.

The results of this study highlight the robustness and scalability of Neo4j in providing accurate and efficient recommendations and demonstrate its potential for use in various business and industry applications.

Tools used

• NEO4J
• Cypher
• GIT