Methods and apparatuses for recommending seasonal items

A method comprising:

• obtaining periodic sales data from at least one database characterizing a number of purchases made of each item of a plurality of items in a specified period;
• obtaining periodic buyers data from at least one database characterizing a number of unique customers of each item in the plurality of items in the specified period;
• obtaining periodic purchase data from at least one database characterizing a count of purchases by each user in the specified period;
• determining, using at least one processor, an item embedding for each item in the plurality of items based on the periodic sales data and the periodic buyers data;
• determining, using the at least one processor, an item seasonality embedding based on the periodic sales data and the periodic buyers data;
• determining, using the at least one processor, a category seasonality embedding based on the periodic sales data and the periodic buyers data;
• blending, using the at least one processor, the item seasonality embedding and the category seasonality embedding using a blending function that weighs the category seasonality embedding based on a number of items in a category to obtain a final item seasonality embedding for each item in the plurality of items;
• determining, using the at least one processor, a user embedding for each user based on the periodic purchase data;
• determining, using the at least one processor, a final user seasonality embedding for each user based on the periodic purchase data;
• generating, using the at least one processor, final user-item scores by applying, for each item in the plurality of items and for each user, a trained machine learning model to a respective item embedding, a respective final item seasonality embedding, a respective user embedding, and a respective final user seasonality embedding, wherein the trained machine learning model is applied by:
• generating an item matrix based on the respective item embedding and the respective final item seasonality embedding,
• generating a user matrix based on the respective user embedding and the respective final user seasonality embedding,
• computing a dot product of corresponding item and user vectors from the item matrix and the user matrix, respectively, to generate a final user-item score,
• wherein the trained machine learning model is trained using the at least one processor based on Bayesian Personalized Ranking and matrix factorization using item pairs and a criterion/loss function;
• and
• sending, using the at least one processor, a recommendation to a user based on the final user-item scores.

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

• obtaining periodic sales data from at least one database characterizing a number of purchases made of each item of a plurality of items in a specified period;
• obtaining periodic buyers data from at least one database characterizing a number of unique customers of each item in the plurality of items in the specified period;
• obtaining periodic purchase data from at least one database characterizing a count of purchases by each user in the specified period;
• determining, using at least one processor, an item embedding for each item in the plurality of items based on the periodic sales data and the periodic buyers data;
• determining, using the at least one processor, an item seasonality embedding based on the periodic sales data and the periodic buyers data;
• determining, using the at least one processor, a category seasonality embedding based on the periodic sales data and the periodic buyers data;
• blending, using the at least one processor, the item seasonality embedding and the category seasonality embedding using a blending function that weighs the category seasonality embedding based on a number of items in a category to obtain a final item seasonality embedding for each item in the plurality of items;
• determining, using the at least one processor, a user embedding for each user based on the periodic purchase data;
• determining, using the at least one processor, a final user seasonality embedding for each user based on the periodic purchase data;
• generating, using the at least one processor, final user-item scores by applying, for each item in the plurality of items and for each user, a trained machine learning model to a respective item embedding, a respective final item seasonality embedding, a respective user embedding, and a respective final user seasonality embedding, wherein the trained machine learning model is applied by:
• generating an item matrix based on the respective item embedding and the respective final item seasonality embedding,
• generating a user matrix based on the respective user embedding and the respective final user seasonality embedding,
• computing a dot product of corresponding item and user vectors from the item matrix and the user matrix, respectively, to generate a final user-item score,
• wherein the trained machine learning model is trained using the at least one processor based on Bayesian Personalized Ranking and matrix factorization using item pairs and a criterion/loss function;
• and
• sending, using the at least one processor, a recommendation to a user based on the final user-item scores.