| CPC G06Q 30/0631 (2013.01) [G06F 18/22 (2023.01); G06N 7/01 (2023.01); G06Q 30/0633 (2013.01); G06Q 30/0641 (2013.01)] | 20 Claims |
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1. 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:
training a machine-learning model to learn item-level embedding Gaussian distributions for items, a user embedding, and product-type embedding Gaussian mixture distributions based on co-purchase item pairs in historic activity data of a user and product-type pairs in an item taxonomy;
receiving a request for personalized complementary item recommendations for an anchor item and the user, the items comprising the anchor item;
generating personalized product-type metrics for the user based at least in part on the user embedding for the user and the product-type embedding Gaussian mixture distributions;
determining top product types based at least in part on personalized product-type complementarity metrics generated using the personalized product-type metrics and cosine similarity measurements;
generating a set of first items of the items associated with the top product types;
ranking each respective item in the set of first items based on a respective item-to-item complementarity metric for each respective item generated using an item-level embedding Gaussian distribution of the item-level embedding Gaussian distributions for the anchor item and a respective item-level embedding Gaussian distribution item-level embedding Gaussian distributions for each respective item; and
selecting a set of top items as the personalized complementary item recommendations based on the ranking.
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