标题： 使用亚马逊产品共购买网络构建推荐系统 显示英文标题

标题： Building a Recommendation System Using Amazon Product Co-Purchasing Network

摘要： 本项目开发了一种面向电子商务平台上新上市产品的在线归纳式推荐系统，重点是向顾客购买其他产品时推荐相关的新兴商品。 我们利用亚马逊产品共购网络元数据集（Amazon Product Co-Purchasing Network Metadata dataset），构建了一个共购图，其中节点代表产品，边表示共购关系。 为了解决推荐信息有限的新产品这一挑战，我们应用了一种经过修改的GraphSAGE方法来进行链接预测。 这种方法通过结合产品特征和现有的共购图结构来预测潜在的共购关系，从而使得模型能够推广到未见过的产品上。 作为一种在线方法，它实时更新，使其具备可扩展性和适应不断变化的产品目录的能力。 实验结果显示，我们的方法在预测相关产品链接方面优于基准算法，为提升电子商务环境中新产品推荐的相关性提供了有前景的解决方案。 所有代码可在https://github.com/cse416a-fl24/final-project-l-minghao_z-catherine_z-nathan.git获取。 显示英文摘要

摘要： 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 baseline algorithms in predicting relevant product links, offering a promising solution for enhancing the relevance of new product recommendations in e-commerce environments. All code is available at https://github.com/cse416a-fl24/final-project-l-minghao_z-catherine_z-nathan.git.