Building a Recommendation System Using Amazon Product Co-Purchasing Network
Minghao Liu, Catherine Zhao, Nathan Zhou

TL;DR
This paper presents an online, inductive recommendation system for new products on e-commerce platforms, utilizing a co-purchasing graph and a modified GraphSAGE method to predict relevant items in real time.
Contribution
It introduces a scalable, real-time recommendation approach for new products using a graph neural network that generalizes to unseen items.
Findings
Outperforms baseline algorithms in link prediction accuracy
Effectively recommends new products with limited initial information
Scales to large, evolving product catalogs
Abstract
This project develops an online, inductive recommendation system for newly listed products on e-commerce platforms, focusing on suggesting relevant new items to customers as they purchase other products. Using the Amazon Product Co-Purchasing Network Metadata dataset, we construct a co-purchasing graph where nodes represent products and edges capture co-purchasing relationships. To address the challenge of recommending new products with limited information, we apply a modified GraphSAGE method for link prediction. This inductive approach leverages both product features and the existing co-purchasing graph structure to predict potential co-purchasing relationships, enabling the model to generalize to unseen products. As an online method, it updates in real time, making it scalable and adaptive to evolving product catalogs. Experimental results demonstrate that our approach outperforms…
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Taxonomy
TopicsDigital Marketing and Social Media
