Systems and methods for determining substitutions

A method being implemented via execution of computing instructions configured to run on one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:

• training, using labeled training data, a machine learning algorithm, wherein the labeled training data comprises positive and negative historical acceptance data that includes at least one of:
• a probability of a similarity between a historical title for a historical item and a historical description of a historical substitute,
• a taxonomy difference for the historical item and the historical substitute,
• a difference between the historical item and the historical substitute, or
• a normalized rank comparison for the historical item and the historical substitute;
• using, based on current data and without requiring use of historical substitute data for an item, the machine learning algorithm, as trained, to determine a ranking of a list of substitutes for the item;
• and
• re-training the machine learning algorithm based on at least the labeled training data and a highest ranked substitute from the ranking of the list of substitutes.

A non-transitory, computer-readable medium comprising instructions that, when executed by a processing resource, cause the processing resource to:

• train, using labeled training data, a machine learning algorithm, wherein the labeled training data comprises positive and negative historical acceptance data that includes at least one of:
• a probability of a similarity between a historical title for a historical item and a historical description of a historical substitute,
• a taxonomy difference for the historical item and the historical substitute,
• a difference between the historical item and the historical substitute, or
• a normalized rank comparison for the historical item and the historical substitute;
• use the machine learning algorithm, as trained, to determine a ranking of a list of substitutes for an item without requiring use of historical substitute data for the item;
• and
• re-train the machine learning algorithm based on at least the labeled training data and a highest ranked substitute from the ranking of the list of substitutes.