| CPC G06Q 30/0631 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01)] | 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 run on the one or more processors, cause the one or more processors to perform:
training iteratively an embedding-based machine learning module to generate a recommendation pool for a reference item, comprising:
converting each item attribute of item attributes into a respective predefined data format of one or more predefined attribute formats;
generating item embeddings based on the item attributes, as converted, for training items;
generating customer embeddings based on customer behavior data for customers;
training the embedding-based machine learning module to determine customer item preference coefficients based on the item embeddings and the customer embeddings;
training the embedding-based machine learning module to determine a respective likelihood of co-purchase between a first item and a second item of the training items for each customer of the customers based on the customer item preference coefficients; and
re-training the embedding-based machine learning module further based at least in part on historic behavior of the embedding-based machine learning module;
determining, in real-time, a diversity preference score for a user based at least in part on: (a) an anchor item chosen by the user via a user interface executed on a user device of the user and (b) a department count for items previously purchased by the user with or after the anchor item, comprising:
determining a respective department for each of a predetermined number of items previously purchased by the user after the user purchased an item in an anchor department of the anchor item; and
determining, in real-time, a count of different departments among the respective departments for the predetermined number of items, wherein:
determining, in real-time, the diversity preference score further comprises determining, in real-time, the diversity preference score based further at least in part on the count of different departments;
determining, in real-time, a comparison result between the diversity preference score and a diversity preference threshold;
generating, in real-time, a personalized recommendation pool based on (a) the comparison result, (b) a complementary recommendation pool generated by the embedding-based machine learning module based at least in part on the anchor item, and (c) a diversity objective function, wherein:
generating the personalized recommendation pool comprises re-ranking recommended items of the complementary recommendation pool based on the diversity objective function and, when the diversity preference score is greater than the diversity preference threshold, the re-ranking includes adding a recommended item one at a time based on a fast greedy maximum a posteriori (MAP) approach and a cross-domain diversity objective function;
the recommended items of the complementary recommendation pool are ranked according to a respective personalized complementary score for each of the recommended items of the complementary recommendation pool;
when the comparison result indicates that the diversity preference score is greater than the diversity preference threshold, the diversity objective function is associated with cross-department diversity for increasing a recommended-item-department count for the personalized recommendation pool; and
when the comparison result indicates that the diversity preference score is not greater than the diversity preference threshold, the diversity objective function is associated with within-department diversity for increasing an anchor-department-item count for the personalized recommendation pool associated with the anchor department of the anchor item; and
transmitting, in real-time, the personalized recommendation pool to be displayed with the anchor item on the user interface.
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