Work

Graph-Based User-Centric Recommender System Using Neo4j, Cypher, and Jaccard Similarity in the Field of e-Commerce

Year: 2025

Type: article

Abstract: This paper presents a scalable and interpretable recommender system architecture that uses a property graph model implemented in Neo4j to generate personalized product recommendations. By representing customers and products as nodes and purchases as edges, and leveraging Jaccard similarity over shared purchases, the proposed system identifies nearest neighbors and recommends items accordingly. The use of graph queries allows for explainability, flexibility and improved performance compared to traditional collaborative filtering methods. Visualization with networkx further enhances transparency. Our research contributes a novel integration of graph-based storage, similarity computation and visual analytics in the domain of recommender systems. We further propose an algorithm for building a graph-based recommendation system for e-commerce to offer personalized suggestions. It is implemented by batching relationships and using the UNWIND clause in Cypher to minimize memory and network overhead. Experimental results transaction data show that the graph-based method delivers interactive performance and high-quality recommendations, which are validated by comparing Jaccard similarity scores with results from the Surprise recommendation library.

Source: IEEE Access

DOI: 10.1109/access.2025.3591705

Authors: Simona-Vasilica Oprea, Adela Bâra

Institution: Bucharest University of Economic Studies

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii