Systems and methods for identifying candidates for item substitution

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

• receiving a test set comprising potential candidate items for substitution for a target item;
• determining association scores for each of the potential candidate items in the test set;
• determining one or more semantic similarity features of the potential candidate items in the test set to the target item in relation to semantic data for the target item and the potential candidate items;
• determining one or more image similarity features of the potential candidate items in the test set to the target item in relation to a similarity of images of the potential candidate items to one or more images of the target item by evaluating image similarity using a convolutional neural network (CNN);
• creating a substitutive probability model by determining a relative contribution of each of the association scores, the one or more semantic similarity features and the one or more image similarity features to a substitutive probability for the potential candidate items in the test set, with reference to a baseline set of the potential candidate items;
• and
• determining substitutive probabilities for an evaluation set comprising candidate items for substitution for the target item by using the substitutive probability model, wherein the candidate items comprise at least one of the potential candidate items.

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

• receiving an evaluation set of potential candidate items for substitution for a target item, the evaluation set comprising one or more of potential candidate items in a test set and non-test set potential candidate items that were not in the test set;
• for any non-test set potential candidate items in the evaluation set that were not in the test set:
• determining non-test set association scores for each of the non-test set potential candidate items;
• determining one or more semantic similarity features of the non-test set potential candidate items to the target item in relation to semantic data for the target item and the non-test set potential candidate items;
• and
• determining one or more image similarity features of the non-test set potential candidate items to the target item in relation to a similarity of images of the non-test set potential candidate items to one or more images of the target item by evaluating image similarity using a convolutional neural network (CNN);
• and
• applying a substitutive probability model created using the test set of potential candidate items to evaluate substitutive probabilities for one or more non-test set potential candidates of the evaluation set, using the non-test set association scores, the one or more semantic similarity features and the one or more image similarity features determined for the one or more non-test set potential candidate items in the evaluation set.