Hierarchical neural network based implementation for predicting out of stock products

A method for implementing a hierarchical neural network (HNN) for predicting out of stock products, the HNN executed by a computer server, the HNN comprising:

• receiving one or more data sources that store disparate datasets having different levels of attribute detail pertaining to products for sale in one or more stores of a retailer, the disparate datasets comprising:
• low-level store data including daily store visits and past occurrences of out of stock products, categorical data including store identifiers and associated cities, store-level product data, and time-series product sales data including monthly or weekly sales per product;
• processing, by a first level, the data from the one or more data sources into respective learned intermediate vector representations, the first level comprising:
• a convolutional neural network to process the low-level store data, an embedding layer to process the categorical data;
• and a long short-term memory (LSTM) layer to process the time-series product sales data;
• concatenating, by a second level comprising a concatenate layer, the respective learned intermediate vector representations into a combined vector representation;
• and
• receiving, by a third level comprising a feed forward network, the combined feature vector representation and outputting 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.

A non-transitory machine-readable storage medium that provides instructions that, if executed by a processor are configurable to cause said processor to perform operations comprising:

• receiving data from one or more data sources that store disparate datasets having different levels of attribute detail pertaining to products for sale in one or more stores of a retailer, the disparate datasets comprising:
• low-level store data including daily store visits and past occurrences of out of stock products, categorical data including store identifiers and associated cities, store-level product data, and time-series product sales data including monthly or weekly sales per product;
• processing, by a first level, the data from the one or more data sources into respective learned intermediate vector representations, the first level comprising:
• a convolutional neural network to process the low-level store data, an embedding layer to process the categorical data;
• and a long short-term memory (LSTM) layer to process the time-series product sales data;
• concatenating, by a second level comprising a concatenate layer, the respective learned intermediate vector representations into a combined vector representation;
• and
• receiving, by a third level comprising a feed forward network, the combined feature vector representation and outputting 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.