Systems and methods for determining substitutions

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:
• determining a list of possible substitutes for an item of a list of items when the item of the list of items is out of stock;
• training a machine learning algorithm, using labeled training data as input and a list of possible substitutes for the item of the list of items as output;
• determining, using the machine learning algorithm, as trained, a respective similarity score for each possible substitute of the list of possible substitutes using the labeled training data;
• determining a respective historical substitution score for each possible substitute of the list of possible substitutes;
• determining a respective final score for each possible substitute of the list of possible substitutes comprises using at least one or more rectifiers having ReLU non-linearity that enables training of deep supervised neural networks without unsupervised pre-training;
• ranking each possible substitute of the list of possible substitutes based upon the respective final score for each possible substitute;
• storing a selection of a highest ranked possible substitute as additional training data with the labeled training data;
• and
• re-training the machine learning algorithm based on the additional training data and the labeled training data.