A Graph Neural Network Approach for Product Relationship Prediction. (arXiv:2105.05881v1 [cs.LG])

Graph Neural Networks have revolutionized many machine learning tasks in
recent years, ranging from 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, or engineering
attributes. They are combined with a classification model for predicting the
existence of the relationship between products. Using a case study of the
Chinese car market, we find that our method yields double the prediction
performance compared to an Exponential Random Graph Model-based method for
predicting the co-consideration relationship between cars. While a vanilla
GraphSAGE requires a partial network to make predictions, we introduce 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. This work provides a systematic method to
predict the relationships between products in many different markets.

Source: https://arxiv.org/abs/2105.05881


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