Seasonality score systems and methods

A computer-implemented method, comprising:

• receiving, from a database, a set of user-specific historical transaction data;
• receiving, from the database, a training data set including a set of historical transaction data associated with a plurality of users over a predetermined time period;
• iteratively training 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 that includes a first parameter characterizing preferences of users to items based on a number of corresponding transactions, and wherein minimizing the first cost function comprises generating each of a first user matrix and a first product type matrix based on the training data, inputting the first user matrix and the first product type matrix into the first cost function, and comparing, by the first cost function, the first parameter with the first user matrix and the first product type matrix;
• iteratively training 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 that includes a second parameter characterizing preferences of users to items based on a number of corresponding transactions, and wherein minimizing the second cost function comprises generating each of a second user matrix and a second product type matrix based on the training data, inputting the second user matrix and the second product type matrix into the second cost function, and comparing, by the second cost function, the second parameter with the second user matrix and the second product type matrix;
• inputting at least first portions of the user-specific historical transaction data to the trained first machine learning model and, in response, generating user-specific seasonal product affinity scores;
• generating a user-specific season affinity score based on the user-specific seasonal product affinity scores;
• inputting at least second portions of the user-specific historical transaction data to the trained second machine learning model and, in response, generating user-specific product affinity scores;
• generating a plurality of user-specific seasonal theme affinity scores based on the user-specific product affinity scores and seasonal product index scores;
• determining a 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;
• comparing 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
• generating, on a 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.

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor cause a device to perform operations comprising:

• receiving, from a database, a set of user-specific historical transaction data;
• receiving, from the database, a training data set including a set of historical transaction data associated with a plurality of users over a predetermined time period;
• iteratively training 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 that includes a first parameter characterizing preferences of users to items based on a number of corresponding transactions, and wherein minimizing the first cost function comprises generating each of a first user matrix and a first product type matrix based on the training data, inputting the first user matrix and the first product type matrix into the first cost function, and comparing, by the first cost function, the first parameter with the first user matrix and the first product type matrix;
• iteratively training 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 that includes a second parameter characterizing preferences of users to items based on a number of corresponding transactions, and wherein minimizing the second cost function comprises generating each of a second user matrix and a second product type matrix based on the training data, inputting the second user matrix and the second product type matrix into the second cost function, and comparing, by the second cost function, the second parameter with the second user matrix and the second product type matrix;
• inputting at least first portions of the user-specific historical transaction data to the trained first machine learning model and, in response, generating user-specific seasonal product affinity scores;
• generating a user-specific season affinity score based on the user-specific seasonal product affinity scores;
• inputting at least second portions of the user-specific historical transaction data to the trained second machine learning model and, in response, generating user-specific product affinity scores;
• generating a plurality of user-specific seasonal theme affinity scores based on the user-specific product affinity scores and seasonal product index scores;
• determining a 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;
• comparing 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
• generating, on a 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.