[3G4-GS-2i] 機械学習：応用

[3G4-GS-2i-03] Store Analysis Using Latent Representation of Robust Variational Autoencoder Based on Showing History Data

〇Ryogo Okubo¹, Ryosuke Uehara¹, Gendo Kumoi¹, Masayuki Goto¹, Tomohiro Yoshikai² (1. Waseda University, 2. Japan Weather Association)

Keywords:Machine Learning, Latent representation, Dimensional compression, Showing history data, Robust Variational Autoencoder

In this research, we propose a model to analyze the characteristics among stores focusing on prepared food products to eliminate food loss, targeting a retail chain with multiple stores. The data is sparse in that the number of prepared food items displayed in each store is only about 10\% of the total number of prepared food items sold in all stores. In order to cope with this sparsity, it is difficult to apply a simple compression method because of the large variation in the input data due to the existence of stores that sell unique products. The latent representation of RVAE is output as a probability distribution, and in general, the similarity is measured by sampling from this probability distribution. The latent representation of RVAE is output as a probability distribution and similarity is measured by sampling from this probability distribution. In this research, we propose a method to calculate the distance between probability distributions without sampling.
We can detect stores with similar tendencies. In addition, the reconstruction error obtained by RVAE enables us to detect stores whose tendency is significantly different from other stores. Finally, we apply the proposed method to real data and verify its effectiveness.