A Graph-based Method for Session-based Recommendations

TL;DR

This paper introduces a graph-based system for session-based recommendations that efficiently manages data and provides effective next-item suggestions without extensive training, using Neo4j and industry data for evaluation.

Contribution

It presents a novel graph-based recommendation method that balances data management efficiency with recommendation effectiveness, implemented with Neo4j and tested on real e-commerce data.

Findings

Effective in real-time data collection and incremental updates

Competitive recommendation accuracy compared to state-of-the-art methods

Demonstrates scalability and efficiency in e-commerce scenarios

Abstract

We present a graph-based approach for the data management tasks and the efficient operation of a system for session-based next-item recommendations. The proposed method can collect data continuously and incrementally from an ecommerce web site, thus seemingly prepare the necessary data infrastructure for the recommendation algorithm to operate without any excessive training phase. Our work aims at developing a recommender method that represents a balance between data processing and management efficiency requirements and the effectiveness of the recommendations produced. We use the Neo4j graph database to implement a prototype of such a system. Furthermore, we use an industry dataset corresponding to a typical e-commerce session-based scenario, and we report on experiments using our graph-based approach and other state-of-the-art machine learning and deep learning methods.