	US 12,079,855 B2
	Seasonality score systems and methods
Luyi Ma, Sunnyvale, CA (US); Nimesh Sinha, San Jose, CA (US); Parth Ramesh Vajge, Sunnyvale, CA (US); Hyun Duk Cho, San Francisco, CA (US); Sushant Kumar, San Jose, CA (US); and Kannan Achan, Saratoga, CA (US)
Assigned to Walmart Apollo, LLC, Bentonville, AR (US)
Filed by Walmart Apollo, LLC, Bentonville, AR (US)
Filed on Dec. 20, 2021, as Appl. No. 17/556,832.
Prior Publication US 2023/0196434 A1, Jun. 22, 2023
Int. Cl. G06Q 30/0601 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0251 (2023.01)

CPC G06Q 30/0631 (2013.01) [G06Q 30/0201 (2013.01); G06Q 30/0256 (2013.01); G06Q 30/0641 (2013.01)]

15 Claims

1. A system, comprising:

a non-transitory memory having instructions stored thereon; and

a processor configured to read the instructions to:

generate a user identifier based on receiving an indication of a specific user interacting with a user interface;

map the user identifier to the specific user;

obtain a set of user-specific historical transaction data associated with the user identifier, the user-specific historical transaction data being generated based on one or more interactions of the specific user with the user interface;

receive a training data set including a set of historical transaction data associated with a plurality of users over a predetermined time period;

iteratively train a first machine learning model based on implicit feedback data and the training data set to find season affinity scores by minimizing a first cost function;

iteratively train a second machine learning model based on implicit feedback data and the training data set to find product affinity scores by minimizing a second cost function, wherein:

each of the first machine learning model and the second machine learning model is a factorization model configured to alternate between solving for a first matrix by fixing a second matrix and solving for the second matrix by fixing the first matrix,

the second cost function comprises a preference for a user u to item I (p_ui) such that p_uiis 1 if a number of transactions is greater than 0 and otherwise is 0, a set of variables measuring confidence in observing p_ui, a u^thcolumn in a user matrix, and an i^thcolumn in a product type matrix;

determine a user-specific affinity score, wherein the user-specific affinity score is associated with the specific user and determined by:

obtaining a set of season to product mappings identifying product taxonomies associated with one or more selected seasons,

inputting the user-specific historical transaction data into the trained first machine learning model to generate user-specific seasonal product affinity scores,

combining the user-specific seasonal product affinity scores with seasonal product index scores to generate a user-specific season affinity score,

obtaining a set of seasonal theme to product type mappings identifying product taxonomies associated with one or more seasonal themes of the one or more selected seasons,

inputting the user-specific historical transaction data into the trained second machine learning model to generate user-specific product affinity scores,

combining the user-specific product affinity scores with seasonal product index scores to generate a plurality of user-specific seasonal theme affinity scores,

computing the user-specific affinity score associated with the specific user based on the user-specific season affinity score and the plurality of user-specific seasonal theme affinity scores;

compare the user-specific affinity score to a predetermined dynamic threshold, the predetermined dynamic threshold being based on one or more external factors associated with the specific user; and

generate, on the user interface, one or more user-specific interface elements if the user-specific affinity score is above the predetermined dynamic threshold and one or more generic interface elements if the user-specific affinity score is below the predetermined dynamic threshold.