A Graph Neural Network Approach for Product Relationship Prediction
A Graph Neural Network Approach for Product Relationship Prediction
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DOI：
10.1115/detc2021-69462
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Faez Ahmed;Yaxin Cui;Yan Fu;Wei Chen
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文献类型：
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作者：
Faez Ahmed;Yaxin Cui;Yan Fu;Wei Chen
近年来，图表示学习彻底改变了许多人工智能和机器学习任务，从组合优化、药物发现、推荐系统、图像分类、社交网络分析到自然语言理解。本文展示了它们在对产品之间的关系进行建模以及对不可见的产品网络进行预测方面的功效。通过将产品表示为节点并将它们的关系表示为图的边，我们展示了名为 GraphSAGE 的归纳图神经网络方法如何有效地学习节点和边的连续表示。这些表示还捕获产品特征信息，例如价格、品牌和工程属性。它们与分类模型相结合，用于预测任意两个产品之间是否存在关系。通过对中国汽车市场的案例研究，我们发现与基于指数随机图模型的预测汽车之间共同考虑关系的方法相比，我们的方法产生的 F-1 分数提高了一倍。虽然普通的 Graph-SAGE 需要部分网络来进行预测，但我们用“邻接预测模型”对其进行了增强，以规避这一限制。这使我们能够在不知道邻居信息的情况下预测产品关系。最后，我们演示了基于排列的可解释性分析如何提供有关设计属性如何影响产品之间关系的预测的见解。总的来说，这项工作提供了一种系统方法来预测复杂工程系统中产品之间的关系。
Graph representation learning has revolutionized many artificial intelligence and machine learning tasks in recent years, ranging from combinatorial optimization, drug discovery, recommendation systems, image classification, social network analysis to natural language understanding. This paper shows their efficacy in modeling relationships between products and making predictions for unseen product networks. By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges. These representations also capture product feature information such as price, brand, and engineering attributes. They are combined with a classification model for predicting the existence of a relationship between any two products. Using a case study of the Chinese car market, we find that our method yields double the F-1 score compared to an Exponential Random Graph Model-based method for predicting the co-consideration relationship between cars. While a vanilla Graph-SAGE requires a partial network to make predictions, we augment it with an ‘adjacency prediction model’ to circumvent this limitation. This enables us to predict product relationships when no neighborhood information is known. Finally, we demonstrate how a permutation-based interpretability analysis can provide insights on how design attributes impact the predictions of relationships between products. Overall, this work provides a systematic method to predict the relationships between products in a complex engineering system.