Assortment optimization involves selecting a subset of substitutable products (subject to certain constraints) to maximize the expected revenue. It is a classic problem in revenue management and finds applications across various industries. However, the problem is usually NP-hard due to its combinatorial and non-linear nature. In this work, we explore how graph concolutional networks (GCNs) can be leveraged to efficiently solve constrained assortment optimization under the mixed multinomial logit choice model. We first develop a graph representation of the assortment problem, then train a GCN to learn the patterns of optimal assortments, and lastly propose two inference policies based on the GCN's output. Due to the GCN's inherent ability to generalize across inputs of varying sizes, we can use a GCN trained on small-scale instances to facilitate large-scale instances. Extensive numerical experiments demonstrate that given a GCN trained on small-scale instances (e.g., with 20 products), the proposed policies can achieve superior performance (90%+ optimality) on large-scale instances (with up to 2,000 products) within seconds, which outperform existing heuristic policies in both performance and efficiency. Furthermore, we extend our framework to a model-free setting where the underlying choice model is unknown but transaction data is available. We also conduct numerical experiments to demonstrate the effectiveness and efficiency of our proposed policies in this setting.
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