Store Analysis Using Latent Representation of Robust Variational Autoencoder Based on Sales History Data

This study proposes an analytical model based on the robust variational autoencoder (RVAE) to analyze store characteristics and further formulate item strategies at the store level for future reduction of the loss of unsold items. The data of the display item vector of each store characterizes the sales strategy of the store. However, it is sparse because the number of items displayed in each store accounts for only approximately 10% of the total number of item brands sold in all the stores. To manage this sparsity, a simple autoencoder model is inappropriate because of the large variation in input data, which is as a result of the store characteristics of selling unique products. Therefore, this study employs the RVAE to analyze the store characteristics of the listed items. Here, the latent representation of an RVAE is usually an output from an estimated probability distribution in the middle layer of the trained network. Generally, the similarity of the latent representations of two input data is evaluated using the sample data of the probability distributions. This study proposes a model to calculate the distance between probability distributions without sampling, for valid estimations of the similarities between latent representations. It proposes a method to use the reconstruction error obtained through the RVAE that enables the detection of stores whose trends differ significantly from those of other stores. Additionally, the proposed method can detect groups of stores with similar trends. In this study, the proposed model was applied to an actual dataset, after which its effectiveness was verified.