Prioritization System for Products Using a Historical Purchase Sequence and Customer Features

In a digital medium product prioritization environment, a recommendation system comprising:

• a purchase model module implemented at least partially in hardware of at least one computing device to determine probabilities of future purchases of products by a customer, the probabilities determined by a multiclass classification to classify the future purchases into groups corresponding to products previously purchased by the customer, the classification based on:
• historical purchase data of the customer indicating the products previously purchased by the customer and when the products previously purchased by the customer were purchased;
• and
• feature data describing characteristics and qualities of the customer;
• and
• a product inclusion module implemented at least partially in hardware of the at least one computing device to:
• generate a ranking of the future purchases based on the determined probabilities of the future purchases;
• compare the determined probabilities to a threshold probability for inclusion in a product group;
• and
• output the product group in a user interface based on the generated ranking and the comparison to the threshold.

In a digital medium product prioritization environment, a recommendation system comprising:

• means for receiving feature data describing characteristics and qualities of a customer and historical purchase data for the customer;
• means for identifying, from the historical purchase data for the customer, one or more products purchased by the customer and when the one or more products were purchased by the customer;
• means for generating probabilities of future purchases by the customer of additional products, the probabilities generated by a multiclass classification to classify the additional products into groups corresponding to the one or more products purchased by the customer based on the historical purchase data and the feature data describing the customer;
• means for determining a subset of the additional products to include in a recommendation by comparing an average of the determined probabilities to threshold probabilities for inclusion in the recommendation;
• and
• means for outputting the recommendation including the determined subset of the additional products in a user interface.