Heterogeneous Graph Neural Networks for Product Recommendation on Transactional Retail Data

Personalized product recommendation is crucial for enhancing user experience and driving sales in e-commerce, yet effectively leveraging sparse, implicit feedback from transactional data remains challenging. This paper investigates the application of heterogeneous Graph Neural Networks (GNNs) for product recommendation on the widely used Online Retail dataset. We frame the task as link prediction between customers and products, constructing a heterogeneous graph from processed transactional records. We employ a GNN model based on GraphSAGE, adapted for heterogeneity, using learnable embeddings derived solely from the interaction structure. Our experiments demonstrate the model's effectiveness, achieving a ROC AUC of 0.853 and an F1-Score of 0.750 (with 0.937 Recall) on the held-out test set using an optimized configuration (negative sampling ratio 1.0, classification threshold -0.5). We analyze the significant impact of the negative sampling ratio during training on the final precision-recall trade-off, highlighting the importance of aligning training parameters with desired recommendation goals. Our findings confirm the viability of heterogeneous GNNs for modeling implicit feedback and providing effective recommendations in a retail context. This paper is organized as follows: Section I introduces the problem and our approach. Section II reviews related work in recommendation systems and graph-based methods. Section III details our methodology, including dataset description, data preparation, graph construction, and the GNN model architecture. Section IV describes the experimental setup and evaluation metrics. Section V presents the quantitative results, including the analysis of parameters like negative sampling. Section VI discusses the findings and limitations. Finally, Section VII concludes the paper.