HIERARCHICAL NEURAL NETWORK BASED IMPLEMENTATION FOR PREDICTING OUT OF STOCK PRODUCTS

    公开(公告)号:US20230267481A1

    公开(公告)日:2023-08-24

    申请号:US17675817

    申请日:2022-02-18

    CPC classification number: G06Q30/0202 G06N3/0445 G06Q10/087

    Abstract: A hierarchical neural network for predicting out of stock products comprises an input layer that receives data from data sources that store disparate datasets having different levels of attribute detail pertaining to products for sale in stores of a retailer. A first level of neural networks processes the data from the data sources into respective learned intermediate vector representations. A second level comprises a concatenate layer that concatenates the learned intermediate vector representations from the second level into a combined vector representation. A third level comprises a feed forward network that receives the combined vector representation and outputs to the retailer an out of stock probability indicating which store and product combinations are likely to have out of stock products over a predetermined timeframe.

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