Embedding-Based Potential Sales Forecasting of Bread Product

Kohei Takahashi, Yusuke Goto

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this study, we investigate the potential sales forecasts of unhandled bread products in retail stores based on factory shipment data. An embedding-based forecasting method that uses large-scale information network embedding (LINE) and simultaneously considers first- and second-order proximities is developed to define similar neighboring stores using their product-store relationship and to predict their potential sales volume. LINE is a network-embedding method that transforms network data into a lowdimensional distributed representation and requires a low computation time, even when applied to large networks. The results show that our proposed method outperforms a simple prediction method (Baseline) and t-SNE, a well-known dimensionality reduction method for high-dimensional data, in terms of accurate product sales prediction via simulation experiments. Furthermore, we conduct a sensitivity analysis to verify the applicability of our proposed method when the forecasting target is expanded to products sold in fewer stores and in stores with less product variety.

Original languageEnglish
Pages (from-to)236-246
Number of pages11
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number2
Publication statusPublished - 2022 Mar


  • bipartite graph
  • bread
  • forecasting
  • network embedding

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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