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A link prediction-based recommendation system using transactional data.

Emir Alaattin Yilmaz¹, Selim Balcisoy², Burcin Bozkaya³

¹Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey. emiralaattin@sabanciuniv.edu.

Scientific Reports

|April 27, 2023

Summary

This summary is machine-generated.

This study introduces a novel recommendation system using transaction data. It effectively predicts user purchases by combining graph learning and gradient boosting, outperforming existing methods.

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Area of Science:

Data Science
Machine Learning
Graph Theory

Background:

Increasing data volume necessitates effective recommendation systems.
Transaction datasets (e.g., credit card, e-commerce) offer valuable user-item interaction signals.
Understanding user interests is key for relevant item recommendations.

Purpose of the Study:

To propose a link prediction-based recommendation system for transaction datasets.
To leverage graph representation learning and gradient boosting classifiers.
To predict future user purchasing behavior, specifically merchant selection.

Main Methods:

Constructing a user-item interaction network.
Applying graph representation learning algorithms for node embeddings.
Utilizing gradient boosting classifiers for link prediction.
Evaluating performance against matrix factorization methods.

Main Results:

The proposed system demonstrated superior performance in merchant prediction.
Key metrics included receiver operating characteristic curves and area under the curve.
Mean average precision also indicated the model's effectiveness.
Transactional data proved powerful for generating relevant recommendations.

Conclusions:

The proposed link prediction-based system excels with transaction data.
Graph learning and gradient boosting offer a robust approach to recommendations.
This method provides a powerful alternative for enhancing user experience in e-commerce and financial systems.